IJERD – International Journal of Environmental and Rural Development (2016) 7-1

Research article
erd

Spatial Assessment of Ecosystem Services by New City
Development – Case Study in Nay Pyi Taw, Myanmar
KAY KHAING LWIN*
Nagoya University, Nagoya, Japan
Email: kaykhaing.moecaf@gmail.com

KIICHIRO HAYASHI
Nagoya University, Nagoya, Japan

MAKOTO OOBANational Institute for Environmental Studies, Japan
Received 15 January 2016

Accepted 11 April 2016(*Corresponding Author)

Abstract Ecosystems provide many services not only for the environment, but also for humanwell-being. Land use and land cover change caused by development activities, have been
increasing rapidly, and these are recognized as key factors for human-induced impacts on
ecosystems. During the past few decades, ecosystem services (ESs) have deteriorated,
especially as a result of urban development at the global scale, and changes have been severe indeveloping countries. For example, in Myanmar, the new capital city was developed on
previously rural land during 2006. Consequently, large land use changes took place and many
ESs were destroyed. To compare the loss of ESs before and after the development, spatial
analyses of ESs were conducted in Nay Pyi Taw, Myanmar, as a case study. For the spatial
analyses, two satellite images; namely Advance Land Observing Satellite (ALOS) for 2010 andLandsat 7 for 1999 were used for land classification. Information on ESs was collected throughliterature surveys, and existing unit values for ESs were estimated by using simple methods.
Then, priority areas for conservation of ESs were identified by evaluating the spatial analysis
results with the Zonation software. Finally, the overall changes in ES provisions due to land usechanges were estimated along with the changes in priority areas.
Keywords ecosystem services, landuse, spatial analysis, conservation prioritization

INTRODUCTION
Ecosystem services (ESs) are the benefits that ecosystems provide to people both directly or indirectly,
and they can be classified into four types, namely, provisioning, regulating, supporting, and cultural
services (Millennium Ecosystem Assessment (MA), 2005).During the past few decades, ESs havedeteriorated because of extensive land use change despite increases in economic and societal prosperity(MA, 2005). Manandhar et al. (2009) demonstrated that land use change is a key driver ofenvironmental change and influences the provisioning of ESs. Methods to identify priority areas for ES
conservation need to be developed (Casalegno et al., 2014). This study attempts to identify priority
conservation areas in Nay Pyi Taw, Myanmar. Kong et al. (2013) evaluated land use change in Nay Pyi
Taw and found that forests have gradually decreased due to agriculture expansion, dam construction,
urban development, and so forth; Nay Pyi Taw is a site of new urban development and thus represents
a good area to analyse the changes in ESs.
OBJECTIVEⒸ ISERD

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The present study aims to understand the spatial distribution patterns of ESs in Nay Pyi Taw, Myanmar,and to identify priority areas for conservation. Comparisons were conducted before and after new
urban development in order to understand the changes in ESs throughout the city and in priority areas.
METHODOLOGY
Study Area
This study was conducted in Nay Pyi Taw which is the new capital city of Myanmar (Fig. 1). It issituated at19°44′42.4"N, 96°07′46.8"E (Nay Pyi Taw City Hall). The population size of the city wasapproximately 1.56 million as of 2014 (Department of Population, 2014) and the city covers a total
area of 7,054 km2.
(b)(a)

Fig. 1 Overview map of Myanmar andFig. 2 (a) Landsat imagery and (b) ALOS
Council and city boundaries of Nay Pyi
imagery in Nay Pyi Taw City in red line
Source: (a) University of Maryland, NASA and
Taw in black and red respectively
GOFC-GOLDand (b) JAXA/Distribution RESTEC

Source: Myanmar Information Management Unit
(MIMU)

Research Flow
This research study was conducted in four parts. The first part involved the collection of satellite
images and the development of land use classifications by supervised and unsupervised classification
procedures. The second part involved the determination of unit values for ESs by literature surveys andestimation of land surface temperatures by using Landsat7 data. For the third part, spatial analyses of
ESs were conducted; and for the fourth part, a comprehensive assessment was conducted by using
Zonation software (Moilanen et. al., 2012) to determine the priority areas for the conservation of ES
provisioning potential.
Land use / land cover classification of satellite image: Land classification was conducted in
ArcGIS10.1. For reference data, Google Earth and original satellite images were used and as anancillary data, Normalized Difference Vegetation Index (NDVI), Digital Elevation Model (DEM), andslope data, calculated from the DEM, were applied. Before final land classifications were made, the
NDVI was used to classify forested and non-forested areas. As there were overlapping values forforested and non-forested areas in the NDVI dataset, slope data were used to further refine the data.
After that, unsupervised classification was used for the Landsat7 1999 image and supervisedclassification was used for the Advanced Land Observing Satellite (ALOS) 2010 image. In this study,
land classifications were categorized into mainly four types, forest, water, urban, and agriculture areas.
Then, an accuracy assessment for the 2010 land classification was conducted. The overall accuracyⒸ ISERD

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was 94.67% based on the ancillary data, such as, google earth.
Satellite images: In this study, two satellite images were used for the analysis. The first satellite image
was taken with the Advanced Visible and Near Infrared Radiometertype2 (ANVIR-2) on board the
ALOS (dated 20101110, here in after, ALOS2010) image and the second image was a Landsat7 image(dated 19991230, here in after, Landsat7-1999). The source of ALOS2010was cited as “satellite image
by JAXA/Distribution RESTEC”; its resolution was 10mx10m (Fig. 2(b)). The source of Landsat71999 was “satellite image by University of Maryland, NASA and GOFC-GOLD”and its resolution is
30mx30m (Fig. 2(a)). In this study, as the resolution of two satellite images is different, land use
classification and spatial analysis of ecosystem services were conducted separately for two images.
After that, the comparison was conducted based on each result of spatial analysis of ecosystem services
and priority maps for two periods.
Types of ESs analyzed in this study and the unit values for each ES: Table 1 shows the types of
ESs and the unit values used for the ESs. For the unit values of ESs, except for temperature regulation,
data were collected through literature surveys. Because the unit values of ESs for Myanmar werelimited in the literature, much of the data was estimated from other Asian regions to have enoughinformation for the analysis. These data were based on elevations, forest types and slope conditions
similar to those in Myanmar. In this study, three major types of ESs, namely, provisioning, regulating
and supporting, were used and cultural services were not included in this study due to data limitation.
Table 1 Types of ESs and the unit values of ESs used in this study
Types of ESs
Supporting Services:
GPP (t/ha/yr)

ForestCarbon stock (t/ha)
Regulating Services:
CO2 absorption (t/ha/yr)
Soilerosionfactor (t/ha/yr)
Water infiltration (mm/h)
Temperature regulation (°C)
Provisioning Services:
Agriculture Products (t / ha)Habitat (GDI)*GPP, gross primary production

Agricultural area

Urban

Water

SourceChenetal.
0(2013),
Hirata et al. (2013)
Oo(2009),0
Takeuchi (2012)

30.7

12.093

0

433.7

31.02

0

2.737
0.038
100
3.86

3.250
3.9
38.5
1.14

0
3
1 15
4.73

-

5.38
-

2
0
-

-

Chen0et al. (2013)
Sidle0et al. (2006)
Chaplot et al. (2002)
Calculated by Landsat7
ETM+
FAO (2009)
Li (2014)

Estimation of temperature regulation: Estimations of land surface temperatures were calculated byusing Landsat7’sEnhanced Thematic Mapper Plus (ETM+) band 6. First, the digital number of band 6
was converted to radiance by using Eq. (1) as follows (Kumar et al., 2012):
𝐿

−𝐿

𝑀𝐴𝑋
𝑀𝐼𝑁
𝐿𝛾 = {𝑄𝐶𝐴𝐿𝑀𝐴𝑋−𝑄𝐶𝐴𝐿𝑀𝐼𝑁} ∗ 𝐷𝑁 − 1 + 𝐿𝑀𝐼𝑁

(1)

The value of LMAX, LMIN, QCALMAX and QCALMIN were obtained from the metadata file of Landsat
image.Secondly, black body temperature was calculated by using Eq.(2) as follows (Kumar, et.al,2012):
𝑇𝐵 =

𝐾2

(2)

𝐾
𝐿𝛾

ln(1+ 1 )The value of calibration constant K1 and K2 were obtained from the Landsat data user’s manual.
Finally, land surface temperatures were calculated based on the Eq. (3) as follows (Yue, et al., 2007):Ⓒ ISERD

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𝐿𝑆𝑇 =

𝑇𝐵

(3)

𝑇
𝜌

1+(𝛾 𝐵 )𝑙𝑛𝜀As land surface temperatures change depend on elevation level, an adjustment was conducted.
Adjustment was made based on “going up 1000 m vertically, temperature goes down 6 degree”.Then,the average temperature in each land use type was calculated and the differences of temperatures
among land use types were obtained as temperature regulation units (Table 1).
Green index (GI): In this study, the GI was calculated to determine the habitat distribution. The GI
was calculated based on the results of land classification maps for Landstat7-1999 and ALOS2010 byusing the resample tool in ArcGIS (Li, 2004). In the GI, green areas and non-green areas wereseparated and assigned values of 255 and 0, respectively. In this study, forest and water areas were
determined to represent green areas and other land uses were categorized as non-green areas.
Mapping with ArcGIS: Spatial analysis was conducted with ArcGIS to understand the spatial
distribution of ES provisions. Seven ES maps were developed in total, and the land classification map
data and unit values of ESs were applied. Before applying the unit values, a grid with a mesh size of
300 m × 300 m was developed for the land classification maps.
Determination of priority areas for conservation: Zonation is a software that can be used for theprioritization of conservation areas (Moilanen et al, 2012) The approach used by this software firstly
sums the value of each ES layer and then discards the least valuable cells one-by-one until all cells are
removed. There are four cell removal rules (Moilanen et al., 2012), and the present study used the
additive benefit function (ABF). The ABF tends to produce a higher average proportion of feature
distributions. In this study, equal weight was used for all ESs. By using the ABF, the present study
achieved the best performance on average for all features or ESs. The main result of the Zonation
analysis was a priority ranking map for the entire landscape.
RESULTS AND DISCUSSION
In the present study, geographic information system (GIS) based spatial analysis was conducted for theassessment of ES provision potential based on land classifications. Figure 3 shows the results of land
classifications for 1999 and 2010.
(a)

(b)

Fig. 3 Land use classification for (a) 1999 and (b) 2010
Based on the results of the land classification, spatial analysis was conducted by using unit valuesof the ESs on a 500 m mesh. The results are shown in Fig. 4. For food production services, the highest
provisioning of the service was in agricultural areas. For the CO2 absorption service, agricultural areas
had higher levels of service provisioning than the forest areas. Perhaps this was related to the unit value
used for CO2 absorption, which was from Chen et al. (2013). Chen et al. (2013) mentioned that
agricultural areas have high unit values because of the chemicals used for the crops. Even though many
peer-reviewed papers were collected and studied to define the unit values, nevertheless, suitable data
for Nay Pyi Taw were difficult to find. Therefore, we just applied the unit value for CO2 absorptionⒸ ISERD

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from Chen, et al. (2013). For temperature regulation, water areas had the highest regulation rates
followed by forest area. For the other remaining ESs in this study, forests were the highest providers ofservices.
The most important and valuable areas in terms of the potential distribution of ESs andconservation of ESs are shown in Fig. 5, and these data were derived from the Zonation analysis. In
Fig. 5, the dark red color represents the top priority areas for conservation and the violet color
represents the lowest priority areas. The top priority areas were mostly forested areas. It is well-known
that forest ecosystems are the main providers of ESs. Therefore, it makes sense that forested areas wereidentified as the top priority areas for the conservation of ESs. In general, the lowest priority areas were
urban developed areas and bare land.
Comparisons of 1999 and 2010 data showed that the amount of priority area decreased in someforested areas and increased in other areas. In 1999, some parts of agriculture areas were sparsely
included in the top priority areas and some parts (mostly northern parts of the study area) were
included in the least valuable areas. Sparse priority areas in 1999 were converted to the least priority
areas in 2010 because of urban development. According to these results, the priority areas identified in2010 should be protected for ES sustainability. Therefore, if infrastructure development projects arebeing planned, these priority areas should be avoided if at all possible.

a) food production 1999b) food production 2010c) CO2 absorption 1999d) CO2 absroption 2010e) soil-erostion control 1999 f) soil-eorstion control 2010g) water infiltration 1999h) water infiltration 2010i) GPP 1999j) GPP 2010k) carbon stock 1999l) carbon stock 2010m) GI 1999n) GI 2010o) temperature regulation 1999 p) temperature regulation 2010

Fig. 4 Spatial analysis results of ESs in Nay Pyi Taw in 1999 and 2010

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(b)

(a)

Fig. 5 Priority rank layer map for (a) 1999 and (b) 2010
CONCLUSION
In this study, a spatial analysis of ESs was conducted in Nay Pyi Taw, Myanmar, which is an area of
rapid urbanization. Most of the ESs analyzed decreased over time, but food production services
increased because of the expansion of agriculture areas. ES provisioning was at the lowest level in theurban development areas. Priority maps were made to identify the areas most important for ES
conservation; much of this land consisted of forested areas.These priority maps should be useful forland use planning, policy development, and protected areas establishment in the Nay Pyi Taw
region.Comparisons between 1999 and 2010 data showed that ES provisioning had decreased,
especially in urban development areas, and several high priority areas in 1999 had changed to low
priority areas in 2010 because of urbanization. Therefore, before any new development projects are
initiated, we should seek to avoid construction in priority areas for ES provisioning as much as
possible. In future study, we seek to include cultural services in this study and also the demand andsupply of ESs will be estimated to be sustainable use of ESs in Nay Pyi Taw region.
ACKNOWLEDGEMENTS
This work was supported by the fund from the EcoTopia Science Institute, Nagoya University.
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