Examining forest cover change and deforestation drivers in Taunggyi District, Shan State, Myanmar

Abstract

Myanmar has been experiencing a significant amount of deforestation and forest degradation in recent years. Being a developing country, people are heavily dependent on its forest for sustenance and livelihood. This study examines a methodology to identify potential drivers and their relative significance for deforestation. The study was tested in one of the districts but could be applied in other areas of the country. The forest and non-forest land cover maps from the Japan Aerospace Exploration Agency (JAXA) for the years 2008 and 2016 were used in the study. It was derived that 46.54% of study area is still covered with forest, but there has been a significant decrease in forest area by 7.29% between the years 2008 and 2016. We examined a number of spatially explicit potential drivers of deforestation such as infrastructure, elevation, slope, deforested land, and population. As informed prevention awareness of deforestation, we projected future forest conditions using a cellular automation modeling technique for the years 2020, 2025 and 2030. We found that major physical and socioeconomic driving factors of deforestation such as proximity to infrastructure (reservoirs and roads), certain elevation levels, slope, proximity to previously deforested area and population density are strongly associated with neighborhood deforestation. The future projection showed a decrease in forest area by 13.8% from 2016 to 2030. This work therefore provides crucial information on forest landscape for forest management in the district. The projective scenario of study area generated by the model highlights the need for forest conservation and planning while addressing the key drivers of deforestation, giving direction for future potential areas of REDD+ implementation in the region.

Full text

Vol.:(0123456789)
Environment, Development and Sustainability (2020) 22:5521–5538
https://doi.org/10.1007/s10668-019-00436-y
1 3
Examining forest cover change anddeforestation drivers
inTaunggyi District, Shan State, Myanmar
PrashantiSharma1 · RajeshBahadurThapa1· MirAbdulMatin1
Received: 20 November 2018 / Accepted: 28 July 2019 / Published online: 5 August 2019
© The Author(s) 2019
Abstract
Myanmar has been experiencing a significant amount of deforestation and forest degrada-
tion in recent years. Being a developing country, people are heavily dependent on its for-
est for sustenance and livelihood. This study examines a methodology to identify potential
drivers and their relative significance for deforestation. The study was tested in one of the
districts but could be applied in other areas of the country. The forest and non-forest land
cover maps from the Japan Aerospace Exploration Agency (JAXA) for the years 2008 and
2016 were used in the study. It was derived that 46.54% of study area is still covered with
forest, but there has been a significant decrease in forest area by 7.29% between the years
2008 and 2016. We examined a number of spatially explicit potential drivers of deforesta-
tion such as infrastructure, elevation, slope, deforested land, and population. As informed
prevention awareness of deforestation, we projected future forest conditions using a cel-
lular automation modeling technique for the years 2020, 2025 and 2030. We found that
major physical and socioeconomic driving factors of deforestation such as proximity to
infrastructure (reservoirs and roads), certain elevation levels, slope, proximity to previ-
ously deforested area and population density are strongly associated with neighborhood
deforestation. The future projection showed a decrease in forest area by 13.8% from 2016
to 2030. This work therefore provides crucial information on forest landscape for forest
management in the district. The projective scenario of study area generated by the model
highlights the need for forest conservation and planning while addressing the key drivers of
deforestation, giving direction for future potential areas of REDD+ implementation in the
region.
Keywords Land cover change· Deforestation· Myanmar· Weights of evidence· REDD+
* Prashanti Sharma
prashanti.sharma222@gmail.com
1 International Centre forIntegrated Mountain Development (ICIMOD), Khumaltar, Lalitpur, G.P.O.
Box3226, Kathmandu, Nepal
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1 Introduction
Forests have extraordinary and far-reaching contribution for the wellbeing of human-
kind and biodiversity. It plays a fundamental role in providing food security, combat-
ing rural poverty and providing livelihood opportunities to a large population of for-
est dependent community. Forests provide a range of long-term ecosystem services like
clean air and water, serve as carbon sinks and store, conserve biodiversity and mitigate
climate change (FAO 2015). Globally, the world’s natural forest area has undergone
major change since 2010 by a decrease of 6.5 million ha per year (FAO 2015). Cur-
rently, deforestation and forest degradation is considered to be the second major con-
tributor of greenhouse gas (GHG) emissions after the fossils fuels (IPCC 2008). Sub-
sequently, Myanmar, a country with about 45% of land covered with forests supporting
variety of distinct vegetation composition and wide range of faunal diversity, is under-
going unprecedented changes in forest quality and quantity in the recent years. Annu-
ally, 546 thousand ha of forest is estimated to be deforested in Myanmar and has been
ranked to have the third highest forest loss in the world after Brazil and Indonesia (FAO
2015). Such substantial loss may bring devastating outcomes to human lives and the
overall ecosystem in the country. With decreasing forest and population growing by one
million annually, pressure for increase in food and firewood consumption is inevitable.
Myanmar has increased 7.73 million ha of agricultural field from 1990 to 2011 which
is the direct result of change in land use pattern. Shan State, a state covering 1/4th of
Myanmar, had the largest net forest loss of 5647.7km2, for the year 2001–2010 (Wang
and Myint 2016) and was responsible for emitting 6.86 million tons of carbon annually
(FAO/RECOFTC 2016).
To reduce the effect of GHG emissions from forests at the same time, contribute to
reducing poverty and sustainable development, “Reducing emissions from deforestation
and forest degradation” (REDD+) developed by parties to UNFCCC has been intro-
duced in many developing countries including Myanmar. The Measurement, Report-
ing and Verification (MRV) activities of REDD+ include timely measurement of forest
stocks, monitoring of forest loss and estimating forest-related emissions, among a few.
For this, earth observation and geospatial technologies can provide spatially explicit
datasets and products useful for monitoring forest cover, identification of drivers of
deforestation and developing forest reference emission levels. Forest cover data prod-
ucts with global coverage like GlobeLand30 (Chen etal. 2015), the Global Tree Canopy
Cover (Hansen etal. 2013), the JAXA ALOS-PALSAR forest/non-forest map (Shimada
etal. 2014), and the Landsat Vegetation Continuous Fields (Sexton etal. 2013) could
provide relevant secondary data for study and analysis (Estoque etal. 2018).Various
geospatial modeling techniques used with these datasets can provide understanding of
complex process of forest cover change, identification of the determinants/drivers of
the change and projecting future scenarios (Thapa etal. 2013). Spatial modeling meth-
ods, logistic regression (Chowdhury 2006; Echeverria etal. 2008), genetic algorithm
(Soares-Filho etal. 2013; Venema etal. 2005), weight of evidence (Maeda etal. 2011;
Soares-Filho et al. 2010; Thapa et al. 2013), and artificial neural network (Khoi and
Murayama 2011; Mas et al. 2004) have been used in the recent past to study complex
process of forest change and associated biophysical and socioeconomic drivers of
deforestation. One of the prominent methods includes Cellular automata (CA) with the
weights of evidence (WofE) modeling framework that is based on Bayes’ rule of prob-
ability with an assumption of conditional independence (Thapa 2012). It identifies the
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interaction between changes in forest cover and the various drivers such as physiogra-
phy, socioeconomic factors, forest management and developmental policies. It has been
widely used for modeling changes in land use and land cover (Bonham-Carter 1994).
Being high on deforestation rates (i.e., 1.8% for 2010–2015), Myanmar has significant
prospects to develop conservation strategies and to study biophysical and socioeconomic driv-
ers of forest change at local level (FAO 2010). Case studies of forest change in the country can
be helpful for gaining an understanding of complex relationships between social and natural
systems that drive landscape change, which is not previously done in the context of Myanmar.
Therefore, in this research, we aim to study the forest cover changes and develop a method
to examine the underlying drivers of deforestation in Taunggyi District of Shan State, Myan-
mar. Emphasis has been laid in spatial analysis of forest changes and examination of spatially
explicit drivers of deforestation. In addition, future forest cover has been projected under Busi-
ness As Usual (BAU) scenario for informed prevention awareness for forest management.
Readily available global land cover maps were assessed; the one with highest comparative
accuracy was used for the study. We employed CA model based on WofE framework to quan-
tify the significant drivers of change and produce future trajectory projection which has the
potential to be applied to other parts of the region and upscaled to country level to produce
broader results, and will be useful in the study of deforestation trends, policy framing and for-
est management.
2 Study area
Taunggyi is a district located in the Southern Shan State of Myanmar (Fig.1). The district
covers an area of 24,239.10 km2 extending from 96°9′46″E, 22°14′45″N to 97°30′38″E,
19°19′3″N. The district is home for 1.7 million people where 27.3% of population living in
urban areas (Ministry of Immigration and Population 2015). Physiographically, the district
is a part of Shan Plateau that ranges between 100 and 2500m. The topography is divided
into Shan Plateau, Myelat and Plains. Myelat and Plains cover 25% of the district, and the
rest are high plateau with mountain ranges, hills and spurs. Plains cover Kalaw Township,
Nyaungshwe and Hopone Townships. Ywangan, Pindaya and Pinlaung Townships are Myelat
region, and the rest are Shan Plateau. Mountain ranges lie from north to south in Taunggyi
District, and the rivers and streams also flow in the same direction. The forest type identified
in the district was Moist Upper Mixed Deciduous Forest, Dry Upper Mixed Deciduous For-
est, Evergreen Forest, Dry Hill forest, Indaing forest and Pine forest. As per land use status of
the district in 1996 there are 18 Reserve Forest covering an area of 30.12% of the district fol-
lowed by 11 protected public forests (2.98%), 11 protected area system (5.19%) and 12 unclass
forest(19.60%) in the district (MONREC 2016). Forests in Taunggyi comprise some of tree
species with high commercial importance such as Tectona grandis, Dalbergia cultrata, Shorea
obtuse, also IUCN “near threatened species” of Dipterocarpus tuberculatus found in the open
and dry forests in the district that is mainly being used for firewood purpose by the locals
(Field survey, December 2017).
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3 Methodology
3.1 Assessment ofland cover data
We accessed and compared three different land cover maps for the study area. These maps
included land cover map provided by Japan Aerospace Exploration Agency (JAXA), for-
est–non-forest (FNF), SERVIR–Mekong regional land cover map (RLCM) and European
Space Agency (ESA) Climate Change Initiative (CCI) for the year 2016. We reclassified
these maps into forest and non-forest. As these maps are in different spatial resolutions,
we have resampled them at 30m for the assessment. For the preparation of reference data,
4000 random sample points at 99% confidence interval with 2% error margin were gener-
ated across the spatial extent of Taunggyi District. These points were then classified into
the forest or non-forest category through inspection in Google Earth Pro. We used the 2016
image in Google Earth to categorize our reference points and used the image of the closest
year in case of non-availability of image for the same year. JAXA FNF map depicted 78%
overall accuracy which was highest as compared to 70% and 66% overall accuracy of ESA
CCI Global land cover map and SERVIR-Mekong RLCM map.
3.2 Determining deforestation driver variables
For modeling deforestation trends, it is important to select best set of input variables in
order to produce the most optimum fit between the empirical data and observed reality.
Therefore, we identified a set of explanatory variables governing the changes from forest
Fig. 1 Study area: Taunggyi District
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area to non-forest, for calibrating deforestation model and quantifying drivers leading to
deforestation. Given this, it must be noted that there are no universal explanatory factors
required for producing ideal simulation results and the set of variables used may not neces-
sarily represent all the factors responsible for the event. The variables may differ from one
landscape to another but similar factors found in many studies conducted by Thapa etal.
(2013), Carodenuto etal. (2015), Chowdhury (2006), Kolb et al. (2013), Seto and Kauf-
mann (2003), Thapa etal. (2013), Elz etal. (2015), etc. We examined factors such as eleva-
tion, slope, proximity to previously deforested area, proximity to roads, proximity to water-
ways, proximity to newly constructed reservoirs, proximity to settlements and local-level
population density (Table1). Physical conditions such as elevation, slopes and rivers play
an important role in land use changes. Elevation differences promote terrain differences
that are associated with complete or selective clearance of forest in Myanmar. Flat land
promotes clearance of forest for agricultural needs, while higher elevations associated with
rugged topography in most cases make it difficult for clearance of forest stands, especially
in the study area. Regions of 100–300m altitude in this region are often associated with
shifting cultivation and conversion of forest land to rubber and palm oil plantation (Myint
2018; Miettinen etal. 2014). Some slopes are subjected to waterlogged conditions, whereas
others provide ideal drainage favoring cultivation in turn contributing to forest clearance as
part of agricultural expansion. Construction of roads has opened up many intact forests for
settlement and resource extraction mostly in the tropical areas. According to the study by
Alves 2002, 90% of deforestation in Amazonian forest occurred within 100 km of road.
Myanmar has been increasing its road network given, the various development projects,
growth in foreign trade and population density. Most of the roads being constructed since
2005 in Myanmar tend to be linked with areas of new rubber plantations, mines and hydro-
electric project sites built at the cost of forested areas, especially in states of Kachin and
Shan. The further expansion in road network is projected to have adverse effect on for-
est, especially the ones being constructed around heavily forested border areas (UN REDD
2017). Hence, proximity to roads is an important determinant that encourages deforestation
arising from higher human accessibility and chances of forest encroachment. Waterways of
all forms like navigable rivers, streams and canals have positive correlation with deforesta-
tion. According to another study by Barber etal. (2014), nearly 95% of all deforestation
in Brazilian Amazon occurred within 5.5 km of roads or 1 km of river. Waterways are
largely used for the transportation of logged materials hence forested river banks in tropi-
cal areas are highly favorable for tree-felling activities. Construction of canals and related
Table 1 Landscape variables used in the study
Variable Source
Elevation SRTM digital elevation model (DEM) (Farr etal. 2007)
Slope Extracted using SRTM DEM
Proximity to previously deforested area Extracted using JAXA FNF map (Shimada etal. 2014)
Proximity to roads Extracted from digitized layer from Google Earth and
MONREC
Proximity to waterways Extracted from digitized layer from Google Earth
Proximity to newly constructed reservoirs Extracted from digitized layer from Google Earth
Proximity to settlement Extracted using data from MONREC
Population density UN-adjusted population density (CIESIN-Columbia 2016)
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anthropogenic activities in the forested regions lead to deforestation and threaten the bio-
diversity of the region. As the population of Myanmar grows by approximately 1.5% annu-
ally, with fuel wood and charcoal consumption being 76.41% of total energy consumption
in Myanmar, higher population density would directly put higher pressure on available land
resources for food and livelihood opportunities (Sein etal. 2015). Hence, population den-
sity of the region is an important driver of deforestation.
Model calibration and simulation were done in Dinamica EGO software that uses
Markov chain approach for calculating landscape transition matrices between the years
(https ://csr.ufmg.br/dinam ica/). The effects of the spatial factors on the landscape change
and probability map of deforestation for those factors were calculated using the WofE
method which is based on Bayesian probability theory. It takes into consideration the def-
erential effect of spatial factors for predicting deforestation (Soares-Filho etal. 2010). The
WofE values represent the attraction or repulsion of deforestation (event) based on the
effect of the driving factor to deforestation. Higher values of WofE (greater than 0) depict
higher probability of deforestation, whereas negative values represent lower deforestation
probability ranges. The variables were tested for spatial dependence using Cramer’s V
(Bonham-Carter 1994) which shows the values ranging from 0 to 1 depicting the degree
of association between the driver factors. Cellular automata (CA) model integrated with
WofE was then used to calibrate deforestation pattern across the district and build BAU
scenarios for upcoming years.
3.3 Transition potential map calculation andsimulation
The model was prepared through the standard calibration, simulation and validation
method. For calibration, the WofE ranges and the coefficients for each of the driving fac-
tors concerning the event, i.e., change of land cover class from non-forest to forest, were
calculated. The WofE coefficients calculated through transitional conditional probability
are significant in understanding the influence of each these drivers. Due to lack of spatial
information on infrastructure development plan, this research assumed that existing infra-
structure will be constant and influence equally overtime. Therefore, all of these drivers
except proximity to previously deforested area were set static during the model run. The
simulation model was then produced by setting internal parameters of patch mean size,
variance and isometry. Since the initial year of land cover map used was 2008, the model
was iterated 8 times to produce a simulated land cover map of 2016. The model validation
included finding change similarity between the observed 2016 and simulated 2016 map
using exponential decay function in Dinamica.
4 Results anddiscussion
4.1 Spatial analysis offorest change
JAXA forest–non-forest map has been analyzed for the year 2008 and 2016 to study the
deforestation over the years. Analyzing the map, it was seen that forest cover in Taunggyi
significantly decreased between the years 2008 and 2016 by 1769.28km2 which is 7.3% of
the forest area. Non-forest category mainly consisting of agriculture, built-up land and tree
cover < 10% has shown an increasing trend from 10,905.83 to 12,624.17km2 at the cost of
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forest land over the years. The water category also depicts an increase of 50.94km2 mainly
because of built-up of new artificial reservoirs in this region in the last years (Table2).
Conversion of land cover from one class to another was also looked upon (Fig.2). The
highest area (3005.84 km2) of inter-land cover class conversion occurred from forest to
non-forest. Forest-to-non-forest conversion is observed, particularly in the southern areas
of the district. Also, area of 54.27km2 of forest area is seen to be converted to water, sig-
nificant portion of it because of the newly constructed reservoirs in the district.
We also analyzed the township-wise change in forest cover in Taunggyi District. The
highest change from forest to non-forest category is seen in the township of Pinlaung where
forest decreased by 893.345km2 at the same time non-forest type has increased to a signifi-
cant amount (Table3). The water category has also increased by a significant amount com-
pared to 2008year mainly accounting to construction of artificial reservoirs in the district.
Reservoir in Thabyegon (Pinlaung Township) has resulted in the clearing of 63.50 km2
of forest area. Townships of Pekhon, Sesai, Ywangan and Kalaw have observed a strik-
ing decrease in forest area over the years. Township of Hopone and Yaksauk surprisingly
shows an increasing trend of forest cover and could be an indicator of good forest practices
at least at the regional level. Hence, expansion of non-forest areas which mainly consist of
agricultural land, urban settlement and barren land have been encroaching fast into the for-
est areas. Construction of new infrastructure such as reservoirs, dams and road network is
also important reason behind such high decline of forest cover in the region.
At national scale, the deforestation trends are alarming. The net loss of forest in Myan-
mar was reported to be 546Kha year−1 between 2010 and 2015 which was 25% higher
than in the 1990s (FAO 2015). Myanmar has undergone agricultural expansion for growing
edible crops like rice, fruits, pulses, etc., and plantation of palm oil and rubber which has
been responsible for about 1 million ha of forest loss between 2002 and 2014 (UN REDD
2017). The deforestation rate of Myanmar has been ranked second among the southeast-
asian countries after Indonesia (684 K ha year−1) and third in the world. In Indonesia,
much of the forested land has been converted to palm oil plantation. Evidence shows that
63% of new palm oil in the country has come at the expense of loss of biodiversity-rich
tropical forest between 1990 and 2010. On the other hand, several countries of South and
Southeast Asia have shown increasing forest trends for the year 2010–2015. The annual
rate of forest gain has been 3.3%, 0.9%, 0.8% and 0.3% in Philippines , Vietnam, China and
India that are included in the top 10 counties with highest net forest gain (FAO 2015).
4.2 Spatial analysis ofdeforestation drivers
The WofE coefficients for each of the 8 variables were derived, where the positive coef-
ficients relate to probability of transition potential favoring deforestation, while the
Table 2 Area and percentage of land cover
Land cover class Land cover area (km2) Land cover percentage (%)
2008 2016 Change (2008–2016) 2008 2016 Change 2008–2016
Forest 13,051.58 11,282.3 − 1769.28 53.84 46.54 − 7.30
Non-forest 10,905.83 12,624.17 1718.34 44.99 52.08 7.08
Water 280.38 331.32 50.94 1.156 1.36 0.21
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negative coefficients indicate that the corresponding variables have potential to repel
deforestation at those value ranges. Table 4 shows the positive coefficients of the 8
variables and their ranges in descending order. The highest WofE coefficient of drivers
variable being distance from the newly constructed reservoirs and could be labeled as
Fig. 2 Land cover conversion between 2008 and 2016
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the driver with the highest impact according to the model. The lowest coefficients are
attained by the variables proximity to existing waterways implying the least impact of
waterways on deforestation as compared to other factors (Table4).
It is evident from the derived coefficients that these geophysical and socioeconomics
drivers of change do have significance on observed deforestation over the concerned
years. If we look at each of these variables separately, it is easier to notice the WofE at
their particular ranges. It is clear from Fig.3 that different factors have varying amounts
of effect on deforestation in either promoting or repelling it at different range intervals.
The peaks (positive values) depict deforestation potential, whereas the lows (negative
values) show ranges that defy deforestation for each variable. Evaluating the drivers of
deforestation, it was found that some drivers like agricultural expansion, infrastructural
development and fuelwood consumption, forest fires, etc., have direct effect on deforest-
ation. Other drivers that relate to social, cultural or technologies factors like population
growth, poverty and policy barriers have indirect effect on forest loss.
Table 3 Township-wise area and
percentage of land cover Townships Total area (km2) Percentage of land cover change
(%)
Forest Non-forest Water
Pinlaung 3395.73 − 26.31 24.44 1.87
Pekhon 2115.01 − 15.56 15.85 − 0.29
Sesai 2079.12 − 13.88 13.90 − 0.02
Kalaw 1449.40 − 9.43 9.42 0.02
Pindaya 629.56 − 6.94 6.94 0.00
Nyaungshwe 1467.49 − 6.79 6.93 − 0.14
Ywangan 2992.38 − 5.28 5.15 0.13
Yauksauk 5152.03 0.34 − 0.28 − 0.06
Taunggyi 2022.58 0.37 − 0.11 − 0.25
Hopone 2935.09 5.27 − 5.28 0.01
Table 4 Weight of evidence of driver variables
v Variables evaluated
a Significant at 95% confidence interval
Variable and unit Ranges Weight of
evidence
coefficientsa
Proximity to reservoirs (m) 0 ≤ v < 100 2.61
Elevation (m) 0 ≤ v < 100 2.25
Population density 220 ≤ v < 240 1.31
Proximity to non-forest (m) 0 ≤ v < 250 0.95
Slope (°) 40 ≤ v < 60 0.6
Proximity to existing settlement (m) 0 ≤ v < 100 0.50
Proximity to existing roads (m) 0 ≤ v < 50 0.10
Proximity to existing waterways (m) 500 ≤ v < 950 0.015
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In the case of elevation (Fig.3a) factor, there has been a significant evidence of defor-
estation in the altitude of 100–200 m ranges with high coefficient of 2.25. This range is
associated with lowland depicting relatively flatter areas in Taunggyi ideal for cultivation,
drained by waterways and has been observed to be used for development of hydropower
projects. The weight then decreases midway between 500 and 1000m elevation with again
a steady increase of up to coefficient of 0.78 at 2500 m elevation. This implies that an
increase in deforestation has been observed at elevation greater than 1000m and could be
a result of forest fragmentation due to shifting cultivation, soil erosion and forest degrada-
tion affecting mostly higher altitude of Taunggyi. In Shan State, one of the major causes
of deforestation is shifting cultivation or slash and burn farming, traditionally known
as “taungya” which has been practiced by ethnic minorities or a long time in hilly areas
(Myint 2018).
The slope factor (Fig.3b) pointed out deforestation to be active at slope range less
than 5 degree depicting most of the flat areas of the regions. This major portion of town-
ships of Taunggyi, Sesai and Yauksauk form ideal agricultural tracts that are constantly
cleared to expand the existing cultivable land. On the other hand, in the regions hilly
terrain small landholdings and population growth force farmers to overexploit the nat-
ural resources cutting more trees for fuelwood and clearing land on steep slopes for
cultivation (Myint 2018). The probability of deforestation is calculated to be highest
(0.6) at 60° slope. Traditional practice of shifting cultivation is mainly operative in the
steeper slopes of the region. This practice is now being replaced by opium cultivation
-1
0
1
2
3
WofE
elevaon(m)
a
-0.2
-0.1
0
0.1
0.2
0.3
0.4
0.5
WofE
slope(degree)
b
-5
-3
-1
1
3
WofE
proximity to previously deforested (m)
c
-0.06
-0.04
-0.02
0
0.02
0.04
0.06
0.08
WofE
proximity to waterway(m)
d
-1
0
1
2
3
WofE
proximity to reservior(m)
e
-0.05
0
0.05
0.1
0.15
WofE
proximity to roads(m)
f
-1
0
1
2
WofE
populaon density (persons/km
2
)
g
-0.2
0
0.2
0.4
0.6
100 300500 700 9001100 1300 1500 1800 51020406080100
250750 1250 1750 2250 3000 4500 8750
50 500950 1400 1500
100 200 300 400500 600900 1000 3100 5100 50 5501000
20 40 60 80 100120 140160 180200 220240 100800 90019002700 4000
WofE
proximity to selement(m)
h
Fig. 3 Weight of evidence coefficients plotted along their range of a elevation, b slope, c proximity to previ-
ously deforested, d proximity to waterway, e proximity to reservoir and f proximity to settlement
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and rubber cultivation and commercial growth of such crops are related to low levels of
development and higher risk to environmental challenges (Myint 2018; UNODC 2017).
Natural or human-induced landslides and forest fires are also important factors for forest
loss on steep slopes.
Non-forest area mostly consists of agricultural land, urban centers and other land not
covered by forest. Regions in proximity to non-forest areas (Fig.3c) have shown high prob-
ability of deforestation, which decreases as the distance increases. The highest weight of
0.95 is nearest to distance of 250m to non-forest land. Hence, expansion of agriculture
over the years could be a cause of deforestation prevalent up to distance of 500m after
which it then gradually repels deforestation. It is generally a notion to clear the surrounding
forest fringe for expansion of its existing agricultural land to feed the growing population.
Most of the forested land in flatter regions of Taunggyi district is lost because of its conver-
sion to cropland, mainly rice and maize. Many parts of the hilly regions in country have
recently undertaken large-scale rubber plantation. There has been a significant increase in
area covered by rubber plantation in Shan State from 4000 to 74,200ha between 2004 and
2005. The southern region of state (Taunggyi District) is characterized by shifting culti-
vation making forest cover highly dynamic. It is estimated that 15,000ha of forest in the
country is affected each year due to shifting cultivation. These have been an important fac-
tor in the increase in non-forested area in the region (UN REDD 2017).
Infrastructural development that includes the construction of reservoirs and artificial
waterways, roads and built-up areas has been on rise in the recent years. Myanmar has
the largest potential for hydropower development in Southeast Asia. Owing to this, many
hydropower projects have been constructed in the country at the expense of forest and
more so within reserved forest and protected forest (UN REDD 2017). It was reported that
110,777 m3 of timber was cleared for hydropower development between 2011 and 2012
(Woods 2015). The factor proximity to the reservoirs (Fig.3e) shows high coefficient val-
ues until 900m of distance from these newly built infrastructures. The positive WofE coef-
ficient in the vicinity of the reservoirs is because of construction of a reservoir in area
previously occupied by forest. This factor can be clearly observed in the case of Taunggyi
District where the construction of reservoir at certain location has disturbed existing forest.
Other commercial land use projects and infrastructural development such as mining and
transportation development through forested areas have adverse effect on the forest health
and in majority of the times result in forest loss. Proximity to waterways (Fig.3d) would
mean clearance of forest in river banks and easy transportation of logged materials through
navigable rivers. It also relates to canals and other water channels which are built con-
necting reservoirs/rivers to agricultural land mostly through the forested areas whose con-
struction is related to deforestation. Through the model calibration, deforestation has been
observed up to a distance of 950m from the waterway quantified by the WofE 0.015. Simi-
larly, construction of roads in heavy forested regions of Myanmar is proven as one of the
direct drivers of deforestation. It is the result of infrastructural extension, resource extrac-
tion, agricultural expansion and providing accessibility to settlement. Construction of high-
ways and major roads (Fig.3f) is observed to be promoting deforestation up to a distance
of 550m up until 1 km. Analyzing the effect of the proximity to settlement (Fig.3h) has
also proved that deforestation is observed in areas closer to settlements. This is one of the
direct drivers of deforestation arising from expansion of urban built-up and increased set-
tlement in rural areas. Myanmar has undergone rapid growth in urban and peri-urban set-
tlement since the last decade, and its built-up areas have increased by 24% from 1992 to
2010, surely at the expense of its forest. Urban settlement increases risk of deforestation of
nearby forest for extension of built-up areas and agricultural land. On the other hand, the
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5532
P.Sharma et al.
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increase in rural settlement areas would mean higher fuelwood dependency to nearby for-
est, leading to greater deforestation and forest degradation rates (Sein etal. 2015).
Population density (Fig.3g) evaluated at an equal interval of 20persons/km2 showed
high degree of deforestation at density greater than 100persons/km2 population density.
It got highest at population density of 240person/km2 showing higher population density
areas evidencing higher deforestation event. In order to meet the demand of growing popu-
lation, large amount of forest land is under stress. Population growth in most cities like Nay
Pi Taw, Yangon and Taunggyi has increased pressure on existing forested land for expan-
sion of infrastructure and meeting agricultural needs. A large portion of rural, peri-urban
and urban population is dependent on forest for their daily fuel consumption. Almost 60%
of the population in the country still rely on forest for fuelwood needs, exerting pressure on
forest resources. The average annual consumption of fuelwood per household is estimated
to be 2.5 cubic tons. With the population projected to increase from 53.9 million in 2015
to 60.2 million by 2030, consumption of fuelwood and charcoal is projected to increase to
55 million m3 by 2030 leading to higher deforestation and forest degradation rates in the
country (UN REDD 2017).
The simulation model produces yearly landscape and probability maps for the given
number of years. The landscape changes every year with the transition probability calcu-
lated for each year. The forest areas with high probability value have a higher chance of
being deforested in the landscape map of simultaneous iterative year. The model can be run
n number of times to produce result of landscape for the nth year. Therefore, to produce
the simulated landscape 2016 map, the model was iterated 8 times based on the probability
of transition for those many years. The spatial independency of the input factor maps was
tested using Cramer’s (Bonham-Carter 1994). This test shows the degree of association
among the factors examined being full or independent ranging from values 0 to 1, respec-
tively. No significant spatial dependency was detected, and the Cramer’s was less than 0.25
in all comparison cases.
4.3 Model validation
Validation of a land change model is usually carried out to determine the prediction abil-
ity of the model by comparing the predicted result to the reference map. Spatial models
are generally validated in the neighborhood context because maps may not always match
pixel by pixel, but they may present similar spatial patterns and spatial agreement within
a certain cell vicinity (Thapa et al. 2013). We used a two-way reciprocal fuzzy similar-
ity method (Almeida etal. 2008) in which the similarity between two maps is considered
based on the influence of the cell and also, to some extent, by its neighbor cell. We com-
pared the spatial similarity between the reference map2016 and the simulated map 2016
at different scales. The exponential decay function was used to incorporate fuzzy similar-
ity method to validate the prediction power of the model. Since it is a two-way reciprocal
fuzzy similarity method, the function compares the differences between the model gener-
ated changes in landscapes and the reference (observed) changes in landscapes.
The similarity of the landscape change is 55% at pixel level and increases to 75.90% at
9*9 window (Fig.4). The window size is further increased to reach a similarity fitness of
87.79% at 21*21 window size. The input land cover resolution was 30m and the window
size 21*21 pixels, so spatial resolution was 630m.
It is seen that with the increasing window size, the similarity between the maps also
increases meaning that the model can predict spatial patterns more accurately at coarser
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Examining forest cover change anddeforestation drivers in…
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spatial resolution. The model users may select the model in different spatial resolution
depending on their scale requirement (Thapa etal. 2013). For example, regional level plan-
ners would require much more detailed results than the province level planners and could
therefore opt for finer resolution results. Since the model depicts fairly accurate level of
compatibility between real and simulated landscapes, it has been further used to develop a
simulation model to depict future forest cover scenarios.
4.4 Forest cover projection
It is also possible to project the forest cover scenarios to future using the historical trend as
part of the simulation model. Figure5 shows the trend of forest cover for the observed year
2016 and simulated years 2020, 2025 and 2030.
Under BAU scenario, the forest area decreased to striking 33.72% in 2030 as compared
to 46.54% in 2016. This decline of 13.72% (3349.84km2) in forest cover is the result of
continuous operation of driving factors and rate of deforestation. From the total forest loss
that occurred between 2016 and 2030, high deforestation tends to affect southern town-
ships of Taunggyi. A closer look into the areas affected by deforestation in each of the
5-year intervals 2020, 2025 and 2030 gives an idea of potential of forest loss in that period
in a greater detail (Fig.6).
In the township-wise analysis of future forest loss scenarios (Fig. 7), it was projected
that 35% of forest loss has taken place in Pinlaung followed by Hopone (12%), Sesai
(11%), Nyaungshwe (10%) and Pekhon (10%). Deforestation tends to affect the southern
townships of Taunggyi since these are the areas where the drivers are most actively operat-
ing. The townships ranked highest to lowest in the case area of forest loss in the projection
Fig. 4 Spatial similarity between
reference map2016 and simu-
lated map2016 at different scales
0
20
40
60
80
100
1234567891
01
1
% similarity (obserevd vs
simulated)
Window Size(cell*cell)
25
35
45
55
65
2008 2016 2020 20252030
Forest cover(%)
Years
Observed Simulated
Fig. 5 Trend in area of forest and non-forest during years 2016–2030
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5534
P.Sharma et al.
1 3
years 2016–2030 remains the same as the observed year 2008–2016 and except the town-
ship of Hopone. Hopone Township which did not experience any significant forest loss in
observed years 2008–2016 has been projected to lose 277.184km2 of forest by 2030.
This could be because of higher proximity to previously deforested land (non-forest in
first iteration) owing to highly fragmented landscape in the township. The driver proximity
Fig. 6 Forest loss for years 2020, 2025 and 2030
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Examining forest cover change anddeforestation drivers in…
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to previously deforested area seems to be operating with high weightage in Hopone Town-
ship with high probability of deforestation in the township. The northern most township
of Yauksauk accounts for least amount of forest loss for the simulated years 2016–2030
since this area consists of dense compact forest with little disturbance from human activi-
ties like construction of reservoirs and roads, fewer settlement and population density of
only 35 persons/km2. High percentage of deforestation in the district is sure to have long-
term effect on biodiversity and regional climate regime. Therefore, it is very important for
immediate public and private intervention to conserve the forest and to discontinue the
present trend of deforestation.
According to the Nationally Determined Contributions, 2016 (NDC) document of
Myanmar, the country formulated several actions relevant to climate change mitigation tak-
ing 2030 as the target year. Forest preservation measures and the action area of “environ-
ment and natural resources” including REDD+ has been in the priority list of the govern-
ment (MONREC 2018). Thus, keeping in mind the changes in the forest area and potential
drivers causing the changes becomes extremely relevant in determining where to lay focus
in the coming years while deciding priority zones and framing action plans.
This study mainly uses the available spatial variables as geophysical drivers of deforest-
ation, but many researches, for example Kolb etal. (2013), used a large number of environ-
mental, biophysical and social variables including landforms, hydrology, ethnicity, migra-
tion and socioeconomic status. This requires more number of data from field study, but as
Pontius etal. (2008) pointed out that a complex model does not necessarily lead to higher
predictive power. Generalized models that are flexible with provision for including increas-
ing complexity are often preferred in planning process. Also, land change models can only
provide projections based upon the quality of inputs provided to the model. There may be
over- or underestimation of future land change due to fluctuations in the annual land change
rates, afforestation programmes or changes in government policy that may impact defor-
estation rates (Elz etal. 2015; Angelsen and Wertz-Kanounnikoff 2008). In addition, there
exist knowledge gaps and absence of data in several areas of work in Myanmar. The extent
Yauksauk
2%
Pindaya
3% Ywangan
4% Taunggyi
6%
Kalaw
7%
Pekhon
10%
Nyaungshwe
10%
Sesai
11%
Hopone
12%
Pinlaung
35%
Fig. 7 Township-wise percentage of forest loss 2016–2030
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5536
P.Sharma et al.
1 3
to which agricultural expansion is happening in non-forest areas or leads to the clearance
of fairly intact natural forests is unknown (UN REDD 2017). Hence, complete understand-
ing and inclusion of other socioeconomic drivers, geological factors, timber trading poli-
cies and forest management practices is important for the development of a more accurate
model. Filling in these gaps would significantly help to more confidently develop measures
and policies for a national REDD+ strategy.
5 Conclusion
The study used readily available JAXA FNF map to analyze the changes in forest cover
and presented a simple and easily replicable model to study future changes for Taunggyi
District. It was derived that the total forest cover declined from 53.84% in 2008 to 46.54%
in 2016 and projected to 32.72% in 2030 in the study area. We quantified factors of defor-
estation and learnt that construction of new infrastructure like reservoirs, certain elevation
levels, slope, population density and distance to previously deforested areas received the
highest weights. Spatial probability map of deforestation was also simulated that helped
demarcate areas with the highest probability to deforestation. A decline of 13.82% of forest
area is simulated for the years 2016–2030.
Since this paper provides a closer look at the drivers and is one of the unique studies
in Myanmar, the amount and quality of geographic data are very limited. This study can
provide basis for other researches in the country. It could be of importance to policy mak-
ers while formation of baseline study, monitoring of forest and formulation of conservation
strategies. As initiative like REDD+ gain momentum, this research can provide for better
understanding of current forest loss, rate of deforestation and identification of critical areas
to be brought under its effect. Since Myanmar has significant potential to reduce its forest
carbon emissions and sustainably manage its forest carbon stocks, it is taking rapid steps
in implementing REDD+ readiness phase. Any REDD+ Demonstration Activity in this
district will need to enhance the protection of existing national parks, implementation of
existing regulations and alternative land use opportunities for locals while addressing key
drivers of deforestation.
Acknowledgements This study was conducted with the financial and technical support from International
Centre for Integrated Mountain Development (ICIMOD). The study owes a lot to the support it received
from ICIMOD’s GIZ REDD+ Himalaya initiative for financial aid. We would like to thank Dr. Bhaskar
Karky for his kind supervision throughout the course of the research and Mr. Niroj Timalsina and Ms.
Shambhavi Basnet for providing assistance in the field and during the research. We are also thankful to For-
est Research Institute (FRI), Myanmar, for their support in the field and Dr. Inkyin Khaine (FRI) for provid-
ing some of the spatial data of Myanmar.
Disclaimer The views and interpretations in this publication are those of the authors. They are not necessarily
attributable to ICIMOD and do not imply the expression of any opinion by ICIMOD concerning the legal
status of any country, territory, city or area of its authority, or concerning the delimitation of its frontiers or
boundaries, or the endorsement of any product.
Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 Interna-
tional License (http://creat iveco mmons .org/licen ses/by/4.0/), which permits unrestricted use, distribution,
and reproduction in any medium, provided you give appropriate credit to the original author(s) and the
source, provide a link to the Creative Commons license, and indicate if changes were made.
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5537
Examining forest cover change anddeforestation drivers in…
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