IJERD – International Journal of Environmental and Rural Development (2015) 6-2

Research articleerd

Comparative Spatial Assessment of Regulating and Supporting
Ecosystem Services 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 13 December 2014

Accepted 31 October 2015(*Corresponding Author)

Abstract Rapid urban development may induce deterioration in biodiversity and ecosystemservices (BD/ESs) at the global scale. According to Millennium Ecosystem Assessment (MA)
2005, it is well known that biodiversity provides benefits in the form of ESs. In particular, fourtypes of ESs (namely, provisioning, regulating, supporting, and cultural services) were definedin a previous study. Here, a case study was conducted in Nay Pyi Taw, Myanmar. First, theprimary unit values of each ES were obtained based on a literature review. Second, by utilisinga satellite image of Nay Pyi Taw from the Advanced Land Observing Satellite, land-use types
were classified into five categories: forest areas, urban areas, agricultural land, water areas, andbare land. Then, the unit values of each ES were applied to develop seven types of ESs andhabitat maps and a priority layer map was created using the Zonation software. The results
revealed the spatial distribution of ESs and the priority areas for conservation. In future, thismethod can be used to consider a wider range of ESs.
Keywords ecosystem services, land-use, zonation, Myanmar, biodiversity

INTRODUCTION
Ecosystem services (ESs) are the benefits provided by ecosystems and can be classified into four typesaccording to MA (2005): provisioning, regulating, supporting, and cultural services. ESs can help for
not only the environment, but also human's livelihoods. During the last decades, ESs has been
destroyed owing to development and increased economic and societal prosperity (MA, 2005). So themethods to identify priority areas for the provision of ESs need to be developed (Casalegno et al.,2014). Although scientific and political interest in ESs has increased, there are gaps in knowledge ofthe spatial distribution of ESs (Garcia-Nieto et al., 2013). In this context, spatial assessment of ESs
provides some indication of the role of spatial flows in the delivery of ESs (Serna-Chavez et al., 2013).
Although many studies have been conducted to assess ESs globally, few such studies have focused on
Myanmar, where 68% of the population live in rural areas (World Bank, 2012). This rural population
typically relies on ESs such as firewood, wood, food, etc. A new capital city, Nay Pyi Taw, wasdeveloped in Myanmar in 2006, with degradation of ESs occurring in the region during the course ofthe development. Accordingly, an understanding of the characteristics of ES provision is important forconservation in this region.

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OBJECTIVE
The present study aims to understand the status of ES provision for different land use types in Nay Pyi
Taw, Myanmar. To achieve this, spatial analysis of ESs was conducted for the region. In mapping ESs,
land use/land cover classification was undertaken to help assess ES provision.
MATERIALS AND METHODSStudy Area
Myanmar, which was formerly known as Burma, is a country in Southeast Asia. Nay Pyi Taw(19°44′40.3"N, 96°07′46.1"E; city hall in Nay Pyi Taw) is a new capital city in Myanmar and wasfounded in 2006 in a previously undeveloped area. The city lies at elevations of 0-500 m and has slope
conditions of approximately 1-20q (FAO, Myanmar country profile). Its population is approximately
1,558,367 as of 2014 (Department of Population, 2014), and it covers a total area of 7,054.37 km2.
(b)

(a)

Fig. 1 (a) Location map of Myanmar and study area and (b) land use/landcover classification map produced by the authors of the present study
Source: satellite image by JAXA/Distribution RESTEC

Research Flow
In the present study, the Advanced Visible and Near Infrared Radiometer type 2 (ANVIR-2) of theAdvanced Land Observation Satellite (ALOS, dated 20101110) was used to classify land use types inthe ERDAS Imagine software version 2014 (Intergraph Corporation) and accuracy assessment wasconducted. Second, primary unit data for several ESs were collected and then applied to the land
classification maps using the ArcGIS10.1 software (ESRI). Third, seven ES maps were developed anda priority layer map was made using Zonation (Lehtomaki and Moilanen, 2013).
Land Use/Land Cover Classification of Satellite Image
Different land use types have different functions and capacities to provide ESs (Burkhard et al., 2012).
Land use classification was conducted using satellite image and was classified into five land use types
using the ERDAS IMAGINE: forest areas, agricultural land, urban areas, water bodies, and bare land
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IJERD – International Journal of Environmental and Rural Development (2015) 6-2(Fig. 1 (b)). The land use classification was conducted using a supervised classification method, which
is a type of land use classification that uses maximum likelihood criteria as parametric rules. The
overall accuracy assessment of the land classification was approximately 91% by comparing withGoogle earth in ERDAS Imagine software and 2010 forest cover map of Myanmar (Forest Department,FRA 2010) in ArcGIS 10.1.
Literature Survey of Ecosystem Services Unit Value
The area-based primary unit values for each ES were obtained based on a literature survey (Table 1). In
particular, unit values regarding provisioning services, regulating services, supporting services, andhabitat were collected. For provisioning services, the unit value of agricultural products (such as riceand pulses) was collected. For regulating services, CO2 absorption, soil erosion control, and waterinfiltration were selected. For the soil erosion data, low values were considered favourable and highvalues were unfavourable; this is in stark contrast to the other ESs considered. Accordingly, the soilerosion data were inversed before being used for analysis. The unit of water infiltration was mm/h andthe percentage of water infiltration by different land use types was calculated by using unit value ofwater infiltration. For supporting services, carbon stock, and gross primary production (GPP) wereselected. According to Rui (2014), Green Distance Index (GDI) evaluates how much green area isconcentrating in each cell for habitat distribution and the GDI is calculated from Green Index (GI)which extracts forests and water as green areas and assigns it as “255” and other land use as “0”. Datadescribing the primary unit values of ESs in Myanmar were limited for this analysis. In the unit valueof ESs in this study, the unit values of carbon stock and agricultural product (food production) were thedata of Myanmar. The other unit values were for other Asian countries in which the forest types, theelevation, and the slope conditions were similar to Nay Pyi Taw Area and were used in this study.
Table1 Types of ESs and primary unit value of ESs in this study
Types of ecosystemservices
Supporting Services:
GPP (t/ha/yr)

Forests

Agriculturalarea

Urban

WaterBareland

Source30.7

12.093000Carbon stock (t/ha)
Regulating Services:
CO2 absorption (t/ha/yr)
Soil erosion factor (t/ha/yr)
Water infiltration (mm/h)
Provisioning Services:
Agriculture Products (t/ha)
Habitat (GDI)

433.7

31.02

000

Chen et al. (2013), Hirata etal. (2013)
Oo (2009), Takeuchi (2012)

2.737
0.038
100

3.250
3.9
38.5

031200150
94.5
2Chen et al. (2013)
Sidle et al. (2006)
Chaplot et al. (2002)-5.38----FAO (2009)
Rui (2014)

Mapping using Arc GIS
Spatial analysis was conducted using ArcGIS to understand the spatial distribution of ES provision andits relationship to land-use type. To develop seven ES maps, the land-classified map and the unit valuesof ESs for each land use type were used. Before applying unit values for the land classification
mapping, a grid with a mesh size of 1 km ×1 km was developed for the land classification map.

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Determination of Priority Areas for Conservation
Zonation is an approach and software for spatial conservation prioritisation, and is used primarily forthe efficient and effective allocation of conservation action. Spatial conservation prioritisation is also aform of conservation assessment (Knight et al., 2006) and incorporates decision support forconservation planning (Ferrier and Wintle, 2009). Zonation firstly sums the value of each ES layer anddiscards the least valuable cell one by one until all cells are removed. Zonation includes four types ofcell removal rules, namely, core area zonation, additive benefit function (ABF), target-based planning,and generalised benefit function; moreover, many parameters are available for analysis (Moilanen et al.,2012). Of the available rules, the present study used ABF to account for the proportions of all maplayers proportions in a given cell. ABF tends to produce a higher average proportion of feature
distributions retained. To weight the ESs in this Zonation analysis, equal weights were selected for allESs. Moreover, although the units of the ESs were different, the normalisation of data was conducted
automatically by Zonation. By using ABF, the present study achieved the best performance on averagefor all features or ESs. The main result of this Zonation analysis was a priority ranking map for theentire landscape.
RESULTS AND DISCUSSION
In the present study, GIS-based spatial analysis was conducted for the assessment of ES provision fordifferent land use types. Fig. 1 (b) shows that forest and agricultural land were the dominant land use
types in the study area, whereas Fig. 2 demonstrates the spatial distribution of ESs in the Nay Pyi Taw
region.
(a)

(b)

(d)

(e)

(c)

(f)

(g)

Fig. 2 Maps of seven ESs in Nay Pyi Taw, showing (a) CO2 absorption, (b) carbon stock,(c) agricultural products, (d) GPP, (e) soil erosion control, (f) water infiltration and(g) habitat (GDI)ⒸISERD162

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In Fig. 2, the red and blue colours indicate high and low values, for ES provision. Overall,
relatively high provisions of ESs, except agricultural products and CO2 absorption, were found inforest areas. Fig. 2 (a) and (c) illustrate the high provision of CO2 absorption and agricultural products
in agricultural areas. In particular, the map in Fig. 2 (a) indicates that agricultural land had a net uptakeof atmospheric CO2. Chen et al., (2013) noted that the carbon sink function of Asian cropland waslikely related to cropland management practices and cropping systems that supplied adequate nutrients,
abundant residues, and organic matter to the soil. This explains the high rates of CO2 absorption forthese agricultural areas. Fig. 2 (b, d, e, f, g) shows the ES provision of carbon stock, GPP, soil erosion
control (inversed value), habitat, and water infiltration. In particular, Fig. 2 (g) shows that habitat is thegreatest in the mesh dominated by forest and water areas. For habitat, GDI was calculated by extracting
the forests and water areas as green area because those areas were good for habitat of the wildlife. In
supporting and regulating services, all of ESs provision is high in the forest areas. So, the forests are
the most important area to conserve to provide ESs and conservation activities of forests need to bepromoted to be sustainable use of ESs.
The main output of the Zonation analysis was a priority rank layer map (Fig. 3 (a)). These results
show the important areas in terms of ES provision, with the priority rank of the site illustrated by acolour scale. The most important and valuable areas in terms of the potential distribution of ESs andconservation of ESs are shown in Fig. 3 (a), and these data were derived from the Zonation analysis. InFig. 3 (a), the dark red color represents the top priority areas for conservation and the violet color
represents the lowest priority areas. In Fig. 3 (a), the top priority areas were mostly forested areas andthe areas located close to forest areas had relatively high priority. It is well-known that forest
ecosystems are the main providers of ESs. Therefore, it makes sense that forested areas were identified
as the top priority areas for the conservation of ESs. In general, the lowest priority areas were urban
developed areas and bare land. Priority maps were made to identify the most important areas for ES
conservation; many of this land consisted of forested areas. Fig. 3 (b) and (c) illustrate another outputof Zonation analysis and Fig. 3 (b) shows the performance of each ESs during prioritization analysis;
and Fig. 3 (c) shows the performance of minimum, maximum, and average distribution of all ESs. Fig.
3 (b) represents that according to the algorithm of prioritization analysis, the lowest valuable cells werefirstly removed and the highest were removed lastly until no cell remains. The horizontal line of Fig. 3(b) means the percentage of the removal of cells (landscape) and the vertical line means the remaining
percentage of each ESs when cells are removed. In horizontal line, “1” means 100% of landscapewhich means “there is no removal of cells” and the dotted line represents the 20% remaining oflandscape before all cells are removed. When the 20% of landscape were remaining, the proportion ofagricultural product was nearly 10% and of soil erosion remained nearly 90%. Fig. 3 (b) and (c) are not
the main results of the analysis and can not be used as effective results. The main result of this analysis
is presented in Fig.3 (a) priority rank map.
(b)

(a)

(c)

Fig. 3 Results of Zonation analysis showing (a) priority rank map and (b), (c) performance
curves, the top 20% of priority areas remain at a level of 0.2 (vertical dashed line)ⒸISERD163

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Finally, using the Zonation conservation planning tool, the priority areas for ES provision weredetermined. Based on the results of the present study, this method will be useful in determining theconservation areas required for ESs and in selecting habitat for multiple biodiversity conservation.
CONCLUSION
Land use change and urban development are likely to accelerate in the coming years. Therefore, theprotection of BD/ESs will become increasingly necessary, particularly in urban areas. In the presentstudy, according to the resulted land use map, the study area is covered primarily by forests andagricultural areas. Based on the results of mapping of ES unit values, forest and agricultural areas had
the highest provisions of ESs. Furthermore, according to the Zonation results, the high priority areas
should be conserved for the sustainable use of ESs in the future. And then, the priority maps should beuseful for land use planning, policy development, and protected areas establishment in the Nay Pyi
Taw region. Also, by using these results, we can remind not to extend the development activities in thepriority areas of ESs provision.
ACKNOWLEDEGMENTS
This work was performed by using the fund from the EcoTopia Science Institute, Nagoya University.
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