Occasional Publication No. 1, September 2015
Centre for Rural Development Studies, College of Natural Resources, Lobesa, Royal University of Bhutan
Land Use Land Cover Changes in Bhutan: 2000-2013
Ugyen Yangchen1, Ugyen Thinley1 and Gudrun Wallentin2
1
2
College of Natural Resources, Royal University of Bhutan, Lobesa, Bhutan
Correspondence: [email protected]
Interfaculty Department of Geoinformatics - Z_GIS, University of Salzburg, Austria
Abstract
The urbanisation and infrastructure development in Bhutan are expanding at a fast pace and it has
been observed that these processes are causing a decrease in the forest cover, agricultural area and
shrub lands. This paper attempts to analyse land use land cover (LULC) changes in Bhutan over a
period of thirteen years from 2000 to 2013. The images of Landsat 5, 7 and 8 were used for LULC
classification and change detection. For validation, ground truth data of 2010 was used for assessing the classification accuracy. The results show that the largest proportion of the area (54%) in
2000 was under vegetation cover, the second largest (20%) was bare area and the third largest
(14%) was under agricultural uses. The built-up areas constituted only 4% and rest was under
snow, rivers and lakes. There was an increase of 10% and a decrease of 6% in the vegetation area
in 2006 and 2013 respectively. Similarly, the agricultural area increased to 15% and then decreased
to 8% during these years. There was a significant increase in the built-up area which accounted for
6% in 2006 and 14% in 2013. Also the snow cover increased from 4% in 2000 to 6% in 2006 and
9% in 2013. The overall accuracy of about 66% for 2000 and 2006 was fairly good. A low accuracy of 52% for 2013 may be partially because the spectral quality of the images was not good
enough and partially due to the fact that the wavelengths of the bands of Landsat 8 satellite are little
different than the corresponding bands of Landsat 5 and 7 satellites. However, the results ca be improved by using advanced methods of image processing and classification.
Key words: Agriculture, Change Detection, Landsat Image, Land Use Land Cover, Maximum Likelihood Classification, Supervised Classification.
Introduction
Bhutan is located in the eastern Himalayas approximately between latitudes 260 41’ 52” N to
0
28 14’ 52” N and longitudes 880 44’ 54” E to 920 41’ 7” E. The total geographical area of the
country is about 38,686 km2 and the elevation ranges from about 160 meters in the southern part
to more than 7000 meters in the northern part. The general climatic conditions are subtropical in
the southern parts, temperate in the central parts and alpine in the northern parts, with a small
area under glaciers and perpetual snow on the higher ranges. According to the Census of Bhutan2005, the total population of the country was 672,425 persons out of which about 70 percent
resided in the rural areas (NSB, 2014). Over the last several years, urbanisation and infrastructure development has expanded at a fast pace due to which not only the forest cover, agricultural
area and shrub lands have decreased but also some other side effects have been observed (Giri
and Singh, 2013). Similar processes have been analysed in the adjoining parts of the Himalayas
and it has been found that a lot of prime agriculture land is lost to the process of urbanisation and
infrastructure development (Prokop and Sarkar, 2012; Regmi, Saha and Balla, 2014; Rimal
2012). The population of Bhutan is projected to reach 809,397 persons by 2020 and it is expected to force significant changes in the land use and land cover (LULC) of the country.
Bhutan is primarily a self-subsistent agricultural economy, engaging more than 70 percent of
the total workforce in this sector alone, but only about 3 percent of the total area of the county is
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under agricultural uses. The Royal Government of Bhutan has developed a vision document
‘Bhutan 2020: A Vision for Peace, Prosperity and Happiness’ (PC 1999) which adopts “Gross
National Happiness as the central development concept based on its four pillars of Equitable and
Sustainable Socio-Economic Development, Environmental Conservation, Preservation and Promotion of Culture and Good Governance and their linkages” (NLC, 2010, p 3). On the other side
‘The Constitution of the Kingdom of Bhutan’ states that “a minimum of sixty percent of Bhutan’s total land shall be maintained under forest cover for all time” (TCoTKB, 2008, p. 12) Although, both the above mentioned declarations seem complimentary but may turn out to be conflicting at some point of time if the land for agricultural expansion and/or developmental activities cannot be obtained from other types of LULC and may force deforestation process.
These concerns raise the significance of regular monitoring of LULC changes in the country.
It is important to keep a balance between the land related needs of the people, urbanisation and
developmental activities on one side and the environmental conservations on the other side. Remote sensing and GIS techniques have proved quite useful for such purposes (Lillesand and
Kiefer, 2000; Yichun, Zongyao and Mei, 2008). However, any studies related to the assessment
of LULC changes in Bhutan is not known to the authors so far. In this paper, change detection in
LULC of Bhutan was performed for a period of 13 years, with a gap of 6-7 years.
Materials and Method
1. Data acquisition
The study is based on the multispectral images of Landsat 5, 7 and 8 satellites (Table 1) acquired from the USGS EarthExplorer (USGS, n. d.) for different points of time. A gap of 6-7
years was desirable for properly detecting the LULC changes in a country like Bhutan where a
very large area is covered by forest and the pace of change is quite slow. The images downloaded from the EarthExplorer pertain to the years 2000, 2006 and 2013. Bhutan is covered by 2 images and the revisit time of Landsat is 14 days so it was not possible to find the images with minimal cloud cover on the consecutive passes of the satellite or even within the same month for
2000. The images were selected for the autumn months because the cloud cover is minimal at
this time of the year and the small vegetation, e.g. grasses and shrubs are dry and the agricultural
fields are fallow, which makes it easier to differentiate between the agricultural fields and the
surrounding grassy areas, but also difficult to differentiate between fallow fields form the bare
soil areas.
Date
28 December 2000
30 September 2000
18 October 2006
9 October 2006
6 November 2013
13 November 2013
Satellite
LS7
LS7
LS5
LS5
LS8
LS8
Path and Row
P – 137 , R – 41
P – 138 , R – 41
P – 137 , R – 41
P – 138 , R – 41
P – 137 , R – 41
P – 138 , R – 41
Ground Coverage
Eastern Half of Bhutan
Western Half of Bhutan
Eastern Half of Bhutan
Western Half of Bhutan
Eastern Half of Bhutan
Western Half of Bhutan
Table 1. Details of the Images Used in the Study
2. Image Processing and Classification
The wave lengths of the bands of Landsat 8 are different than the Landsat 5 and 7 satellites
(Table 2). The wavelength of Band 1 of Landsat 8 is 0.43-0.45 micro meters and this does not
exist in the images of the other two satellites (USGS 2014) so this was excluded from the processing of the images. The six closely matching bands common in all the images were selected
i.e. bands 1, 2, 3, 4, 5 and 7 for the images of Landsat 5 and 7; and bands 2, 3, 4, 5, 6, 7 for
Landsat 8 images. The selected bands of each image were composited in ESRI Software ArcGIS
38
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Editors: Shahnawaz and Ugyen Thinley
Occasional Publication No. 1, September 2015
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version 10.1 the two images of the same year were mosaicked to make one complete image covering whole Bhutan. However, an area of about 85 km2 on the south-western boundary of the
country falls in the third scene, i.e. path 139 and row 41, but this was left out from the study. The
National Boundary of Bhutan was used to extract the required parts from the mosaicked images.
Band
Coastal Aerosol
Blue
Green
Red
Near Infrared
(NIR)
Cirrus
SWIR 1
SWIR 2
Panchromatic
Thermal Infrared
(TIRS) 1
Thermal Infrared
(TIRS) 2
Wave Length in Landsat 8 (µm) Wave Length in Landsat 5 and 7 (µm)
0.43 - 0.45
0.45 - 0.51
0.45-0.52
0.53 - 0.59
0.52-0.60
0.64 - 0.67
0.63-0.69
0.85 - 0.88
0.77-0.90
1.36 - 1.38
1.57 - 1.65
2.11 - 2.29
0.50 - 0.68
1.55-1.75
2.09-2.35
0.52-0.90
10.60 - 11.19
10.40-12.50
11.50 - 12.51
Table 2. The Bands and their Wavelengths in Different Landsat Satellites
The extracted images of Bhutan for each of the three years were classified by applying maximum likelihood method of supervised classification technique. Ten classes were identified on
each image (Table 3), out of which only seven classes pertained to LULC and the remaining
three consisted of noise elements such as clouds, shadows and stripes etc. Since the spatial resolution of Landsat images is approximately 30 meters and it is difficult to clearly differentiate
between some classes having similar optical characteristics. Due to this, the high resolution images on ArcGIS online and Google Earth as well as the ‘Bhutan Land Cover Assessment (LCA)
2010’ data generated by the Ministry of Agriculture and Forests (MoAF, 2010) were referred to
minimise the errors in defining the training areas for each class. In this way, thirty training areas
were identified for each class on each image and these were used for generating the signature
files for classifying the images.
Class ID
Class Name
Class Description
1
2
Snow
River
3
Lake
4
Vegetation
5
6
7
8
9
10
Agriculture
Bare Area
Build-up Area
Shadow
Cloud
Others
Areas covered by snow
Areas covered by flowing water
Areas covered by marshy area. water reservoir
and water other than flowing river
Broad leaf + conifer+ fir+ chirpine+ spruce forest
+ shrub+ meadow
Dry land+ wet land+ orchards
Rocky outcrop+ bare soils+ degraded area
Settlements+ industrial + commercial+ facilities
Shadow
Cloud
Noise elements other than shadow and clouds
Table 3. Image Classification Schema
Reclassified ID
for Masking
1
2
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3
4
5
6
7
0
0
0
Occasional Publication No. 1, September 2015
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It is evident from Table 3 that there were some non-LULC classes. i.e. noise elements, in all
the classified images. The IDs of the three such classes, i.e. Shadow (9), Cloud (10) and Others
(11), were reclassified to zero and the ‘Bhutan LCA 2010’ dataset was used to mask these areas.
The ‘Bhutan LCA 2010’ dataset has been generated from the images having 10 meter spatial
resolution captured by the Advanced Land Observing Satellite (ALOS - AVNIR-2) during the
winter season from 2006-2009 (MoAF, 2011). The process of preparing ‘Bhutan LCA 2010’ also
included extensive ground truthing and other sources of spatial information. Thus, this dataset
served as the best source for masking as well as a base for testing if the training samples defined
on one dataset can be transferred for classifying another dataset. This procedure was used for
accuracy assessment of the classified Landsat images.
Before using for masking and accuracy assessment, the ‘Bhutan LCA 2010’ dataset required
some processing. It had 28 LULC classes whereas the classification schema used in this study
has only seven LULC classes (Table 3). The current classification schema was taken as the base
and the relevant classes of ‘Bhutan LCA 2010’ were merged together to match the classes of all
the images. The merged dataset of ‘Bhutan LCA 2010’ was resampled to match the resolution of
the classified Landsat images of the three years being studies.
3. Accuracy Assessment
The accuracy assessment is necessary to validate the results of image classification and several methods have been developed for this process (Banko, 1998; MGL, 2013; Verbyla, 2013). As
mentioned above, the merged and resampled dataset of ‘Bhutan LCA 2010’ was used as test
ground. The sample size of the test points / pixels was derived based on the thumb rule that these
should at least be ten times the number of classes for each class (VGE, 2013). As there are seven
LULC classes so the total sample size was computed as 7*10*7 = 490 pixels, and the numbers of
pixels for each class were determined by using ratio calculation. All the seven LULC classes
were assigned a rank in the ascending order of their area, each rank was divided by the sum of
the ranks, i.e. 28, and finally it was multiplied by the total number of pixels, i.e. 490 (Table 4).
Classes
Snow
River
Lake
Vegetation
Agriculture
Bare Area
Built-up Area
Total
Ratio
4
3
1
7
6
5
2
Sample size
4/28*490 = 70
3/28*490 = 52.5 (52)
1/28*490 = 18
7/28*490 = 122.5 (122)
6/28*490 = 105
5/28*490 = 87.5 (88)
2/28*490 = 35
490
Table 4. Sample Size of Test Points for Accuracy Assessment
Result and Discussion
1. LULC: Area and Change
The results of LULC classification show that vegetation occupied 54 percent of the total area
in 2000, which was the largest among all the classes (Table 5 and Map 1). The vegetated areas
were spread as continuous stretches all over the country except for the northern and western
parts. These bare areas account for 20 percent of the total area which have high altitude, cold
climate and rocky surface, and all of these factors do not support the growth of vegetation. The
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agriculture area of 14 percent was distributed in the form of elongated stretches and their shape
is self-explanatory that most of these are situated in the flatter valley floors. Some patches of
agriculture area were also visible in the northern and western cold-rocky parts, which seems to
be a classification error due to similarity in the spectral values of some soil deposits in the higher
dry valleys with the fallow agricultural fields. The 4 percent snowy areas are visible along the
northern boundary of Bhutan. The proportion of the remaining three classes, i.e. lakes, rivers and
built-up area, is 1 percent, 2 percent and 5 percent respectively which is distributed in small
patches in different parts of the country and is hardly visible.
Class Name
Snow
Lake
River
Bare
Vegetation
Agriculture
Built-up
TOTAL
%Area
2000
4
1
2
20
54
14
5
100
%Area
2006
6
1
2
6
64
15
6
100
Change
2000-06
2
0
0
-14
10
1
1
0
%Area
2013
9
1
3
7
58
8
14
100
Change
2006-13
3
0
1
1
-6
-7
8
0
Change
2000-13
5
0
1
-13
4
-6
9
0
Table 5. Bhutan: Land Use Land Cover - 2000, 2006 and 2013
The vegetation area showed a significant increase of ten percent between 2000 and 2006 (Table 5 and Map 2). There was also an increase of 1 percent in agriculture area, 2 percent in snow
area and 1 percent in built-up area during this period. Contrary to this, bare area decreased by 14
percent over the same period. A comparison of Map 1 and Map 2 shows that the bare areas disappeared from the central parts of the country and the vegetation areas extended northward and
westward into the bare areas. This seems an effect of the dates of image capturing (Table 1). The
images used for 2000 were captured on 30th September and 28th December, and the vegetal cover
in the higher altitude areas would have changed drastically over these 3 months. Also the images
used for 2006 were captured on 9th and 18th October which falls in between dates of the 2 images
of 2000 and there will be certain differences in the vegetal cover.
During 2006 and 2013, built-up area shows a significant increase of 8 percent whereas the area under vegetation and agriculture shows a decrease of 6 percent and 7 percent respectively
(Table 5 and Map 3). Some of these changes can be attributed to the difference of about 3 weeks
in the image capturing dates and some is a real change due to conversion of land from one type
to the other. An increase of 3 percent in the snow covered area seems to be the result of early
snowfall i.e. in November (Map 3).
The overall analysis of LULC during 2000 to 2013 shows that vegetation covered the largest
area in all the three years but its proportion fluctuated from 54 percent in 2000 to 64 percent in
2006 and to 58 percent in 2000. The variability in coverage can be attributed to phenological
effect of vegetation such as shedding of leaves by deciduous trees during winters. As mentioned
previously, one image of 2000 was from December and both the images image for 2013 were
from November. Apart from this, some rapid economic developments took place which had entailed increase in infrastructure development in the country (GNHC, 2015). These included mega
hydro power projects, power transmission lines, industries, widening of national highway and
farm roads to provide better facilities for rural development. All such activities certainly have a
bearing on the LULC changes.
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A significant decrease was found in agriculture area from 14 percent to 8 percent and an increase in built-up area from 5 percent to 14 percent (Table 5). These changes can be attributed to
the population growth in the country which was estimated as 677,934 persons in 2000 (CSO,
2000), enumerated as 672,425 persons in 2005 (OCC, 2005) and projected as 733,004 persons in
2013 (NSB, 2013). In particular, the growth of urban areas due to increasing economic opportunities and rural-urban migration had forced the construction of a large number of buildings for
serving as residences, offices and business places. Also the expansion of facilities and amenities,
infrastructure, roads, drinking water pipelines, and recreational sites is causing reduction in agriculture areas. Such developmental activities have led to significant conversion of agricultural
lands in to built-up areas.
Also a high decrease in bare areas from 20 percent in 2000 to 7 percent in 2013 was found.
The larger coverage of bare areas in 2000 can be attributed to the exposure of rocks with less
snow cover during the month of September. Further, images from November and December
could have led to mixing of spectral properties between agriculture area and bare area due to
non-agriculture season on one side and dryness on the other side.
Map 1. Bhutan Land Use Land Cover – 2000
2. LULC: Accuracy Assessment
As mentioned in the methodology, an attempt was made to test if the classification validation
test areas created on a single and different dataset can be used for validating the classified images of various years. The classification validation test pixels were extracted from ‘Bhutan LCA
2010’ and these were used for assessing the accuracy the 3 LULC datasets of 2000, 2006 and
2013. The results show that the overall accuracy of classification for the dataset of 3 years, i.e.
2000, 2006 and 2013, was 66.32 percent, 66.60 percent and 52 percent respectively (Tables 6, 7
and 8). The overall accuracy for each of the years, especially year 2013, was way below 80 percent which according to Kiefer and Lillesand (2000) is said to be the threshold of good classification result. This can be associated with the fact that the satellite images used for classification
were captured on dates and months as the images of good spectral quality of the same months
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were not available from the USGS EarthExplorer portal. Besides, the images were from non–
agriculture months (October, November and December) when the agricultural fields have no
crops and it becomes difficult to distinguish agriculture fields from bare soils or also from shrubs
sometimes.
Map 2. Bhutan Land Use Land Cover – 2006
Map 3. Bhutan Land Use Land Cover - 2013
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LULC Class
Snow
River
Lake
Snow
River
Lake
Vegetation
Agriculture
Bare areas
Built-up areas
Total
Producer’s accuracy
Overall
47
5
0
0
4
10
3
69
68.12
0
13
0
2
8
10
20
53
24.53
0
2
8
2
2
2
2
18
44.44
Vegetation
0
0
0
104
9
3
3
119
87.39
Agriculture
0
1
0
6
79
9
7
102
77.45
Bare
Area
4
2
0
4
11
59
8
88
67.05
Built-up
Area
0
0
0
0
22
2
11
35
31.43
Total
51
23
8
118
135
95
54
484
User’s
accuracy
92.16
56.52
100.00
88.14
58.52
62.11
20.37
66.32
Table 6. Confusion Matrix for LULC - 2000
Classification
Snow
River
Lake
Snow
River
Lake
Vegetation
Agriculture
Bare areas
Built-up
Total
Producer’s accuracy
Overall
52
2
0
3
1
9
2
69
75.36
0
10
0
2
8
6
27
53
18.87
0
6
9
0
1
0
2
18
50
Vegetation
0
4
0
90
15
3
8
120
75
Agriculture
0
0
0
7
89
3
3
102
87.25
Bare
Area
3
6
0
5
10
54
10
88
61.36
Build-up
Area
0
0
0
2
14
0
19
35
54.29
Total
55
28
9
109
138
75
71
485
User’s
accuracy
94.55
35.71
100.00
82.57
64.49
72.00
26.76
66.60
Table 7. Confusion Matrix for LULC - 2006
Classification
Snow
River
Lake
Snow
River
Lake
Vegetation
Agriculture
Bare areas
Built-up
Total
Producer’s accuracy
Overall
49
1
0
1
0
10
8
69
71.01
0
5
0
5
9
3
31
53
9.43
2
1
14
0
1
0
0
18
77.78
Vegetation
0
3
0
74
9
10
23
119
62.18
Agriculture
0
0
0
9
80
6
7
102
78.43
Bare
Area
24
10
1
2
10
12
29
88
13.64
Build-up
Area
0
0
0
0
17
0
18
35
51.43
Total
75
20
15
91
126
41
116
484
User’s
accuracy
65.33
25.00
93.00
81.32
63.49
29.27
15.52
52.07
Table 8. Confusion Matrix for LULC - 2006
The satellite images captured during different seasons are considered important for classifying
agriculture area but such Landsat images of usable quality were not available due to large cloud
coverage and haze etc. (Prishchepov et al., 2012). Apart from this, low resolution (30m) of
Landsat images with no field verification can result in low accuracy (Yichun, Zongyao and Mei,
2008; Prishchepov et al., 2012).
Conclusions and Outlook
A very large proportion of the total area of Bhutan has extensive vegetal cover. Although,
there were some fluctuation in this area between 2000 and 2013 still it covers 58 percent of the
country’s area, which is 4 percent more than that in 2000. The built-up areas are increasing due
44
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to urbanisation and developmental processes and these are encroaching up on the areas under
vegetation and agriculture. There is no significant change in the water bodies like lakes and drivers but there is a small increase in the area under snow cover. A significant decline in bare areas
seems to be an error in the classification caused by the fact that there is significant gap in the
dates of capturing the images used in this study.
The overall accuracy of the classification varied between 52 percent and 67 percent, which is
considered quite poor, and the accuracy of some classes was as low as 20 percent. This happened
due to several confounding factors which include dates of the images, coarse resolution of Landsat images as well as testing of a methodology for transferring the validation samples taken from
a different dataset than from the classified images to be validated.
Remote sensing technology offers a range of advantages over the conventional methods of
studying LULC changes. Given the availability of good quality data and application of appropriate methodology, this technology helps in reducing costs and time to a large extent. However,
this study confronted several limitations due to which authors resorted to depend on the images
of different spatial and temporal resolutions. For example, the images on Google Earth and
ArcGIS Online were of varying spatial resolutions depending on the scale of visualisation and
for different points of time, and these were referred during the process of identifying LULC classes on Landsat images of 30 meter resolution. In addition, the ‘Bhutan LCA 2010’ was of 10
meter resolution and this was used as a proxy ground reference for validating classification accuracy. This approach was adopted due to sheer lack of time and resources. Also advanced using
image processing methods, such as band ratioing for detecting clouds and shadows as well as
NDVI computation, can facilitate better discrimination between vegetation and other cover types
and help in reducing misclassification or classification errors (Farooq, n.d). The results can be
improved tremendously by overcoming the limitations and using advanced methods of image
processing and classification.
Acknowledgements
This study was carried out as part of the project ‘Geospatial Methodology to Assess Climate
Change Adaptation Strategies for Traditional Economy in Bhutan’ co-funded by the Commission
for Development Research (KEF), Vienna, Austria and coordinated by the Interfaculty Department of Geoinformatics - Z_GIS, University of Salzburg, Austria. The authors are grateful to
KEF as well as to Z_GIS for involving the College of Natural Resources (CNR), Royal University of Bhutan (RUB) as a partner in this project and providing ample opportunity to benefit from
the faculty development activities. We are also thankful to the Austrian Development Cooperation Coordination Office, Bhutan for co-funding the dissemination conference and to the Centre
for Rural Development Studies, CNR, RUB for publishing this paper in the conference proceedings. We highly appreciate Dr. Shahnawaz (Z_GIS) for his support and guidance throughout this
research project.
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Farooq, N. (no date). Band Ratioing . <www.geol-amu.org/notes/m14a-4-8.htm>. Accessed 7 September
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45
Proceedings of the Conference on ‘Climate Change, Environment and Development in Bhutan’
2-3 April, 2015, Royal University of Bhutan (RUB), Thimphu, Bhutan
Editors: Shahnawaz and Ugyen Thinley
Occasional Publication No. 1, September 2015
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46
Proceedings of the Conference on ‘Climate Change, Environment and Development in Bhutan’
2-3 April, 2015, Royal University of Bhutan (RUB), Thimphu, Bhutan
Editors: Shahnawaz and Ugyen Thinley