IJERD – International Journal of Environmental and Rural Development (2017) 8-1

Research article
erd

Spatial Assessment of Land Cover Change and Ecosystem
Services from a Case Study in Savannakhet Province, Laos
THIPPHACHANH SOUPHIHALATH*
Nagoya University, Nagoya, Japan
Email: poumpui@gmail.com

KIICHIRO HAYASHI
Nagoya University, Nagoya, Japan

MAKOTO OOBANational Institute for Environmental Studies, Miharu Japan

WATARU KOBAYASHI
Nagoya University, Nagoya, Japan
Received 15 December 2016

Accepted 5 May 2017(*Corresponding Author)

Abstract Land use without proper land management may cause deforestation and othernegative impacts on ecosystem services (ESs), as defined in the Millennium Ecosystem
Assessment of 2005. In Laos, natural resource sectors have received many investments in thepast decades, during which a loss of ESs occurred. The aims of this study were to assess
impacts on the potential provisions of ESs caused by land cover changes. The Savannakhet
province in Laos was selected for the case study. Land cover changes were investigated using
Landsat satellite images from 1988. A land cover map for 2010 was obtained from thegovernment. After classifying the image into five land cover types, spatial analyses of the ESs
were conducted by utilizing the primary unit values of six ESs. The results indicated that forests
were converted to agricultural areas. Most of the potential provisions of ESs decreased due tothese conversions in the past two decades.
Keywords ecosystem services, land cover change, spatial analysis, Laos

INTRODUCTION
Land use without proper land management may cause deforestation and other negative impacts on
ecosystem services (ESs) as defined in the Millennium Ecosystem Assessment (MA, 2005).
Appropriate land use management practices are required to provide adequate ESs. Goldewijk andRamankutty (2004) and Chase et al. (2000) determined that land cover change was a key driver ofenvironmental and ES changes. Numerous studies have focused on the relationship between land coverchanges and the loss of ESs (Kay Khaing Lwin et al., 2016). An understanding of the spatial patterns
associated with ESs could be applied for developing effective environmental policies and for decisionmaking (Izquierdo and Clark, 2012). In Lao People's Democratic Republic, land use changes
associated with potential development might cause negative impacts on ES provisions (USAID, 2015;
Yoshida et al., 2010) and natural resource sectors such as agriculture and forestry have received many
investments in the past decades (Saunders et al., 2014). In Laos, Savannakhet province located in thesouth of the country received eight percent of the total foreign direct investments from 2004 to 2010,
two-thirds of which were provided to the agriculture and forestry sectors (IUCN and NERI, 2011).
This may cause agricultural expansion and forest degradation in this province. There are fewⒸ ISERD

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IJERD – International Journal of Environmental and Rural Development (2017) 8-1

researches conducted in this province. Therefore, a study of the spatial impact assessment by land
cover changes is needed, with a focus on the changes of ES potential provisions in Laos, especially inthe province. The results of the ES study could be applicable to land use planning and other decisionmaking processes related to ES conservation in this area.
OBJECTIVE
The objective of the study is to assess impacts on the potential provisions of ESs caused by land coverchanges in the past two decades in Savannakhet province, Laos. A spatial analysis of ESs was
conducted based on comparing a 1988 land cover map with those of a 2010 map.
MATERIALS AND METHODS
Study Area
Savannakhet province is the largest province in area in Laos and is located in the southern part of thecountry (the Administration Office of the province: 16°34′15.2″ N, 104°45′48.3″ E) (Fig. 1).
The average annual temperature was 26.2 ℃, and the average annual rainfall was 1,672.3 mm (Lao
Statistics Bureau, 2013). The total population was 969,700 (Lao Statistics Bureau, 2015), and the area
covers 21,774 km2, 90 % of which is flat (IUCN and NERI, 2011). The elevation of this study area
ranges from 62 m to 255 m, based on the ASTER GDEM (ASTER Global Digital Elevation Model:
http://www.jspacesystems.or.jp/ersdac/GDEM/E/index.html)

Fig. 1 Maps of Laos (on the left) and Savannakhet province (on the right)
Note: Black and red lines showed the country and the study area boundaries.

Research Process
The study consisted of three steps. The first step was the acquisition of satellite images and thedevelopment of a 1988 land cover map. Then we performed a reclassification of a 2010 land cover map
provided from the Forest Inventory and Planning Center (FIPC), Government of Laos. The second step
was an estimation of ES supply potentials by utilizing each ES unit value based on a literature survey,
a land surface temperature calculation using band 6 (thermal band) of a Landsat 5 Thematic Mapper(TM) images, and the Universal Soil Loss Equation (USLE) analysis. Finally, after the normalizationof the ES values by utilizing equal weights for each ES unit, a comprehensive analysis was conducted
to understand the impacts on the ESs caused by land cover changes in the past two decades. ArcGIS
10.4.1 (ESRI) was used for the analysis.
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1988 Land Cover Map Development and Reclassification of 2010 Land Cover Map
The following process was conducted for the development of a 1988 land cover map. Landsat 5 images
for 1988 at 30 m x 30 m resolution were downloaded from the United States Geological Survey
website (http://landsat.usgs.gov). Four satellite images (WRS path/row 127049 and 127048 dated 16th
March and 16th Feb. 1988, 126049 and 126048 dated 6th Feb. 1988) covered the study area. The images
were subset to the study area located in the western part of the province, shown as a red line in Fig. 1.
The images were classified into five types of land cover classes: forest, dry-forest, water, urban andbare land, and agriculture area. The normalized difference vegetation index (NDVI) and the normalized
difference water index (NDWI) (McFeeters, 1996) were used to separate forest and water, respectively.
For the classification of the remaining classes, namely, mixed-forest, dry-forest and grassland, urban
and bare land, and agriculture area, supervised methods were used. Subsequently, the mixed-forestclass was integrated into the forest class. Swamp areas were difficult to identify in the 1988 images;
therefore, swamps were classified as “Other” together with clouds which were excluded from the ES
analysis.
An accuracy assessment was conducted for the 1988 classification using ancillary data by creating
approximately 100 random points for each land class, except urban and bare land class, which only 35
points were set due to the small ratio of urban area against the total area. Then the random points of the1988 map were compared with 1988 Google Earth image and original satellite images as references.
The result of the accuracy assessment was 0.853 by the kappa coefficient (Table 1). However, theresolution of the reference is not high. But we could not find any other reference for the old map.
We used a land cover map of 2010 provided by the FIPC, and reclassified the existing 22 land
cover types into the same five land cover types used for the 1988 map (Table 2).
Table 1 Accuracy assessment of the 1988 land cover map
ForestClassified
Forest1988
Dry-Forest
land
Water
cover
Urban and Bare land
map
Agriculture area
Total
Kappa coefficient
Overall accuracy

90
7
0
3
0
100

1988 Google Earth image
DryUrban and
Water
ForestBare land
10
0
0
90
0
0
4
88
0
3
0
26
9
0
0
116
88
26
0.853
86.7 %

Agricultur
e area
0
3
8
3
92
106

Total
100
100
100
35
101
436

Table 2 Reclassification of the 2010 land cover map
National Level Classification System (NLCS) for Laos 2010 by FIPC
11Evergreen Forest, 12Mixed Deciduous Forest, 14Coniferous Forest, 15Mixed Coniferous andBroadleaved Forest, 16Forest Plantation, 21Bamboo, 22Regenerating Vegetation
13Dry Dipterocarp Forest, 31Savannah, 32Scrub, 41Grassland
81Water
71Urban, 72Barren Land and Rock, 80Other Land
51Upland Crop, 61Rice Paddy, 62Other Agriculture, 63Agriculture Plantation
42Swamp, 82Cloud, 83Cloud Shadow
Note: Land cover numbers were defined under NLCS by FIPCⒸ ISERD95

New category for thisstudy by authors
ForestDry-forest
Water
Urban and bare land
Agriculture area
Other

IJERD – International Journal of Environmental and Rural Development (2017) 8-1

Table 3 shows the ES unit values based on a literature survey conducted by Kay Khaing Lwin etal. (2016) with several adjustments for this study, except temperature regulation and USLE estimation.
The ES categories from the MA (2005) were used, including provisioning, regulating, and supporting
services, except for cultural services. For supporting services, gross primary production (GPP) andcarbon stock were selected. For regulating services, temperature regulation (land surface temperature
estimation: LST), USLE and water infiltration were chosen. For provisioning services, only
agricultural products (averaging per ha production of local products, namely, rice, vegetables, maize,
soil bean, peanut, starchy roots from Lao Statistic Bureau (2013)) were used.
Table 3 Primary unit values of ESs
Types of ESs
Supporting Services:
GPP (t/ha/yr)1
Carbon stock (t/ha)2
Regulating Services:
Water infiltration (mm/h)3
Temperature regulation (°C)
Provisioning Services:
Agriculture Products (t /ha)

Agriculture Urban and
Water
area
bare land

ForestDry-Forest

30.7

4.585

12.09300131.61

41.19

0

0

0

100
2.93

280
1.06

38.5
0.26

2
0

-

4.00

-

0
Chaplot et al. (2002)
5.23 by authors*
Lao Statistic Bureau(2013)-

SourceChen et al. (2013) and
Hirata et al. (2013)
Forest Carbon (2015)

Source: Kay Khaing Lwin et al. (2016) revised by the authors,*: developed by using Landsat 5 as explained in the land surface temperature estimation section below.
1: Data were attempted to collect in the study area or nearest location. GPP for forest was obtained from Hirata et al. (2013)’
Thailand data which was close to this area, and Chen et al (2013) estimated in GPP of Asian region that were used for dryforest and agriculture area.
2: Carbon stock unit values in Laos were presented in Forest Carbon (2015) which used biomass equations originally from
Chave et al. 2005 and 2014. Then the unit values for forest and dry-forest in this paper were revised by taking into
consideration the ratio of evergreen forest and deciduous forest areas in Savannakhet comparing 2000 and 2010 values(NLCS for Laos 2000 and 2010 by FIPC).
3: Water infiltration unit value of tree plantation from Chaplot et al. (2002) was used for those of forest unit value in this study,
and the fallow and pastures unit values in the same paper were used for dry-forest in this study. For agriculture area, theaveraged figures of cultivated (corn, vegetables) and cultivated (with conservation) in Chaplot et al. (2002) were used.

Land Surface Temperature (LST) estimation: LST for 1988 and 2010 were calculated by using theband 6 images of Landsat 5 TM. Four image scenes were used for making the 1988 land cover map,
but only two scenes of Mekong River side where majority of urban areas situated, were utilized in thisLST estimation for each year, which included enough urban area with other land cover types. Then, thefollowing 16 images in separate eight years were collected: for 1988 LST estimation (Path/Row of
127/049: 16 Feb. 1988, 11 March 1989, 18 Feb. 1990, 10 April 1991; and those of 127/048: 16 March
1988, 11 March 1989, 19 Dec. 1990, 10 April 1991) and for 2010 LST estimation (Path/Row of
127/049: 5 March 2007, 24 April 2008, 25 Feb. 2010, 28 Feb. 2011; and those of 127/048: 1 Feb. 2007,
24 April 2008, 25 Feb. 2010, 28 Feb. 2011). These images were used to calculate LST as a shot of LST
in different time in each year. Most of the images were captured around 10a.m. in Laos local time. The
images were acquired in dry season from January to April or beginning of May to avoid the effect ofclouds. The following processes as described by Kumar et al. (2012) originally from NASA (2004)
were used to estimate LST. The first is Eq. (1).
LJ­½
LMAX  LMIN®
¾ u DN  1  LMIN
¯ QCALMAX  QCALMIN ¿(1)

Note: LMAX=the spectral radiance that is scaled to QCALMAX in W/(m2 u sr u 𝜇m), LMIN=the spectral radiance that isscaled to QCALMIN in W/(m2 u sr u 𝜇𝑚 ), QCALMAX=the maximum quantized calibrated pixel valueⒸ ISERD

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IJERD – International Journal of Environmental and Rural Development (2017) 8-1(corresponding to LMAX) in DN=255, QCALMIN=the minimum quantized calibrated pixel value (corresponding toLMIN) in DN=1, DN=Digital Number

Table 4 Parameter values of Landsat 5 TM
Satellite/Sensor
Landsat 5 TM
Source: the Landsat 5 TM metadata fileLMAX
15.303

LMIN
1.238

K1
607.76

K2
1260.56

The values for LMAX, LMIN, QCALMAX, and QCALMIN were obtained from the Landsat 5 TM
metadata in Table 4.
After converting DN to Lγ, the black body temperature TB was calculated using Eq. (2) which iscalled Plank's inverse function (Kumar et al., 2012).
TB

(2)

K2
§ K ·
ln ¨1  1 ¸
¨
LJ ¸¹
©

The values of K1 and K2 were obtained from the Landsat 5 TM metadata file in Table 4. TB wasconverted to LST using Eq. (3) (Yue et al., 2007; Kumar et al., 2012).
LST

TB
§ T ·
1  ¨¨ J B ¸¸ ln H
© U ¹

(3)

Note1: 𝛾=the wavelength of emitted radiance (𝛾 = 11.5 𝜇𝑚), 𝜌 = ℎ u 𝑐/𝜎 p=1.438 u 10-2 (mK), h=Plank`s constant (6.626
u 10-34 Js), c=the velocity of light (2.998 u 108 ms-1), 𝜎=Boltzmann`s constant (1.38 u 10-23 JK-1), 𝜀=spectral emissivity(vegetation=0.95, non-vegetation=0.9, water=1.0 (Nichol 1996))
Note2: LST results for 1988, 2007 and 2008 contained error points because of including little cloud part, the results weremodified by excluding those parts out before adjustment by DEM data.

After converting the sea level elevation value for each pixel of the LST value by assuming as aconstant of 6.5 /km lapse rate (a surface temperature decreases with an increase in altitude) (Minderet al.,2010), the LST values were averaged in each land cover type separately. Then the LST value
differences were calculated based on the difference between the averaged LST value of urban areas andthose of other land cover types in each year separately. Finally, the ES unit values for temperature
regulation were estimated by averaging the LST value differences for eight years by each land covercategory (Table 3).
USLE: USLE is based on a formula to estimate the quantity of soil erosion per unit area caused bysurface water (precipitation) (USDA, United States Department of Agriculture: http://www.usda.gov/wps/portal/usda/usdahome). It was calculated using Eq. (4). The R value was calculated using Eq. (5)(Kobayashi et al., 2002). A in Eq. (5) used rainfall data from Savannakhet Meteorology Station by the
years of 1990 and 2010. The K value was calculated by Eq. (6) to Eq. (10) (Wawer et al., 2005
originally by Williams, 1995) based on the soil map of Laos obtained from the FAO (Food andAgriculture Organization of the United Nations: http://www.fao.org/soils-portal/en/). The L and S
values are topographical factors and were calculated in ArcGIS using the ASTER GDEM by a simple
method, LS calculated just from the slope value in % using Eq. (11) (Mediavilla et al., 2017 originallyby Ededo et al., 1995). The C value was obtained from Dubber and Hedbom (2008). The P factor was
not considered. Hence, assuming that P = 1 (constant) when no soil conservation measures werepracticed.
E Ru K u Lu S uC u P(4)Ⓒ ISERD

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IJERD – International Journal of Environmental and Rural Development (2017) 8-1

R

§ A ·
0.32 u ¨¸© 100 ¹

K

KW

2.5

(5)
(6)

f csand  f cl si  f orgc  f hisandf csand§ªm º·¨ 0.2  0.3  exp « 0.256  ms  §¨1  silt ·¸» ¸¨© 100 ¹¼ ¸¹¬©

f cl si§ msilt ·¨¨¸¸© mc  msilt ¹

f orgc§·0.25  orgC
¨¨1 
¸¸
>
@
orgC

exp
3
.
72

2
.
95

orgC
©
¹

f hisand

§
·
m ·
§
¨
¸
0.7  ¨1  s ¸
¨
¸
100 ¹
©
¨1 
¸
ª
º
m
m
§
·
§
·
¨ ¨1  s ¸  exp « 5.51  22.9  ¨1  s ¸» ¸
¨ © 100 ¹© 100 ¹¼ ¸¹
¬
©

LS

(7)

0.3

­0.009 u p 2  0.0798 u p,®
¯0.2558 u p  3.248,

(8)
(9)

(10)

p d 30%(11)

p ! 30%Note: E: mean annual soil loss (t/ha/year), R: rainfall erosivity factor (MJ・mm/ha/h/year), K: soil erodibility factor (t・h/MJ/mm), L: slope length factor, S:slope steepness factor, C: crop management factor, P: erosion control practicefactor, A: Annual rainfall (Savannakhet Meteorology station by statistic yearbook 2010), ms: the sand fraction content(0.05-2.00 mm diameter) (%), msilt: the sand fraction content (0.002-0.05 mm diameter) (%), mc: the sand fraction
content (<0.002 mm diameter) (%), p: slope (%), orgC: the organic carbon (SOC) content (%).

ESs Mapping
The land cover maps and the ES unit values were used to create maps for the potential provisions ofESs. A grid with a mesh size of 500 m × 500 m was created. A comprehensive analysis was conducted
for the 1988 and 2010 maps by averaging each normalized ES score (0 to 1 scale). However, the USLE
was converted to be a minus value before normalization because the original USLE value was high ES
value in low USLE figure.
Table 5 Percent of land cover in 1988 and 2010Land cover type
ForestDry forest
Water
Urban and bare land
Agriculture area

1988 (%)
51.18
36.60
1.33
0.05
9.83

Ⓒ ISERD982010 (%)
42.31
28.00
2.06
0.42
27.21

IJERD – International Journal of Environmental and Rural Development (2017) 8-1

RESULTS AND DISCUSSION
Table 5 and Fig. 2 show the results of the land cover classifications. Forest and dry-forest areas
decreased considerably and in contrast, agricultural area increased markedly from 1988 to 2010. In
order to compare both maps, the areas of cloud and swamp parts which were classified as “Other”
category in the 2010 map were excluded from the 1988 map as well. The results of the spatial analysis
are shown in Fig. 3. Some ES supply potentials have decreased especially for carbon stock and GPP
because of the decrease of the forest areas in general. As agricultural area increased, the provision offood production has also increased. Fig. 3c) and 3d) defined the change of soil erosion quantities, thered color of the USLE maps was identified the lowest annual soil loss which means soil loss of the2010 map was smaller than that of the1988. Water area had the highest value with regard totemperature regulation service, followed by forests (Table 3). Surface temperatures changed
considerably between 1988 and 2010 mainly because of the impact from land cover change especiallyfor the center and southwest parts where the transformation from forest and dry-forest to agriculturalarea have happened, which had the higher potential provisions of its service in forest and dry-forest
than agriculture area. As consequent, as shown in the Fig. 3g) and 3h) the temperature regulation
service was decreased in general. Water Infiltration service was identified as water infiltration capacity
which also affected from land cover changes. As shown in the Fig. 3i) and 3j), water infiltration service
in the southwest part of the study area was declined due to the change from dry-forest to agriculturalareas, followed by forest transformation to agricultural areas.

Fig. 2 Land cover classification (a) 1988 and (b) 2010
The results of comprehensive analysis were presented in Fig. 4(a) and 4(b) to prioritize ES
conservation needs. The overall values of ES supply potentials were higher for 1988 than for 2010.
Entirely, forest area had the highest importance of ES potential provisions in both maps. Therefore, theperspective of nature conservation, a land use policy sector should consider it properly for their future
land use development policy, for example, land concession for industrial plantations which need thehuge area of their cultivation. It will be one of the policy options to limit land use types, for example,
introducing land use zoning policy, to protect forest area in order to avoid reducing the potential
provisions of ESs.

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a) food production 1988b) food production 2010c) USLE 1988d) USLE 2010e) GPP 1988f) GPP 2010g) temperature regulation 1988h) temperature regulation 2010

Fig. 3 Maps of ESs in the province for 1988 and 2010 [a) - h)]Note: Map of USLE Fig. 3c) and 4d) were higher values in the blue color which means higher area of soil erosion quantities(this means low ES provision)Ⓒ ISERD

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i) water infiltration 1988j) water infiltration 2010k) carbon stock 1988l) carbon stock 2010

Fig. 3 cont. Maps of ESs in the province for 1988 and 2010

(a)

(b)

Fig. 4 Maps of comprehensive assessments for (a) 1988 and (b) 2010: 0 to 1 scale
CONCLUSION
A spatial analysis of potential provisions of ESs was conducted to understand the distributions of theESs and to determine priority areas for conservation of the ESs. Forest area decreased considerablyfrom 1988 to 2010 in contrast to agricultural area. Most of the changes in land cover type came fromthe transformation of forest and dry-forest to agricultural areas, and this might be the main cause forthe changes in the potential provisions of ESs over two decades. Most of the potential provisions of
ESs decreased due to these changes. The present study could be a useful tool for land use planning
based on prioritizing conservation areas for ESs in this region. Our results illustrate the importance ofconsidering ESs into land-use planning and land management policy. This approach could apply not
only for this study area but also throughout nationwide and could probably be applicable for otherⒸ ISERD

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developing countries. Future issues that require further study include the increased number of ESs, thecollection of ES unit values for Laos, and the increase in the number of land cover classes. Culture
services are also important in terms of social environment to understand the ES influence on their local
livelihood activities.
ACKNOWLEDGEMENTS
The authors thank the Forest Inventory and Planning Center (FIPC), Government of Laos for providing the2010 land cover map.
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