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American Journal of  
Geospatial Technology (AJGT)

Mapping and Monitoring Change Detection of  High-Altitude Wetland Using Remote 
Sensing and GIS: A Case Study of  Rara Wetland, Nepal

Srijan Thapa1*, Riya Pokhrel1

Volume 4 Issue 1, Year 2025
ISSN: 2833-8006 (Online)

DOI: https://doi.org/10.54536/ajgt.v4i1.4005
https://journals.e-palli.com/home/index.php/ajgt

Article Information ABSTRACT

Received: November 14, 2024

Accepted: December 24, 2024

Published: February 07, 2025

High-altitude wetlands (HAW) are essential for the environment as they mitigate climate 
impacts, facilitate water regulation and groundwater recharge, function as carbon sinks, and 
promote ecological stability. To protect it from disappearing, it is essential to map and moni-
tor change detection to analyze the extent. However, research on monitoring HAWs remains 
limited in the world and gets very limited attention. This research uses Geographic Infor-
mation System (GIS) and remote sensing techniques to analyze the change in Rara wetland 
extent from 2000 to 2020. We used the Shuttle Radar Topography (SRTM) digital elevation 
model to generate slope and Topographic Wetness Index (TWI) and Landsat 7 ETM+ imag-
es to derive normalized difference Vegetation Index (NDVI), Normalizing Difference Water 
Index (NDWI) and supervised classification for different years. Over the past two decades, 
1.25% of  the total area of  Rara wetland has been lost at an annual rate of  0.997 hectares. It 
highlights the urgent necessity for conservation action. This study confirms that GIS and 
remote sensing effectively delineate, map, and assess wetland dynamics, offering essential 
insights for future conservation policies and strategies.

Keywords
GIS, High Altitude Wetland 
(HAW), Rara, Remote Sensing, 
Spectral Indices

1 International Centre for Integrated Mountain Development (ICIMOD), Kathmandu, Nepal
* Corresponding author’s e-mail: shrijanthapa01@gmail.com

INTRODUCTION
Wetland ecosystems are the most endangered 
environmental resource, which covers approximately 6% 
of  Earth’s land (Kerry Turner, 1991). Natural wetlands 
are shrinking globally, as in the 1970 and 2015, inland 
and coastal wetlands have decreased by about 35%, 
(Ramsar Convention on Wetlands, 2018). This rate is 
three times greater than the rate of  forest loss. With this, 
many critical ecosystem services are lost, such as water 
and air purification, biodiversity protection, groundwater 
recharge, food security, and habitats for many endangered 
species (Gong et al., 2010). 
High-altitude wetland (HAW) is defined as “areas of  
swamp, marsh, meadow, fen, peatland or water located 
at an altitude above 3000 m, whether natural or artificial, 
permanent or temporary, with water that is static or flowing, 
fresh, brackish, or saline”(Chatterjee et al., 2010). These 
fragile ecosystems are crucial to providing hill communities 
and large populations in the plain ecological and economic 
security, regulating water flow, protecting biodiversity, acting 
as carbon sinks to mitigate climate changes and delivering 
essential ecosystem services (Trisal & Kumar, 2008); 
However, HAWs face threats from catchment degradation, 
changes in hydrological regimes, human-induced pressures, 
and climate change impacts, and their vital ecosystem 
services have not been adequately recognized.
In Nepal, four of  the nine Ramsar sites are high altitude 
wetlands: Rara, Phoksundo, Gosaikunda, and Gokyo. 
The government has taken some initiatives like the 
National Wetland Policy (2012), the Ramsar wetland site 
establishment, and the formation of  Wetland Management 
Committees (WMC) at the grassroot level. This shows 

the efforts have been primarily focused on only policy 
measures and not on change detection of  wetlands. 
For wetland monitoring, the Geographical information 
system (GIS) and remote sensing with Landsat data have 
been used by numerous studies all around the world 
(Ghobadi et al., 2012; Habiba et al., 2011; Teferi et al., 2010; 
Wu, 2018). These tools offer the advantage of  large-scale, 
repeated coverage, and comprehensive analysis making 
them ideal for monitoring spatial-temporal dynamics of  
wetland distribution.
The major objective of  this research is to utilize 
geographic information systems and remote sensing 
techniques to map and detect the change in HAW (Rara 
wetland) and provide valuable insight that will contribute 
to the conservation efforts of  wetland ecosystems and 
future conservation strategies and policies.

MATERIALS AND METHOD
Study Site
The research area is in Mugu district of  western Nepal, 
which stretches from 81˚59’45.48’’ to 82˚19’ 14.36’’ 
East longitude and 29˚ 24’ 12.44” to 29˚ 39’ 57.25” 
North latitude cross citation, which covers around 480 
km2. Rara Lake, the focus point in this wetland area, is 
Nepal’s biggest lake. It is located at an altitude of  2990 
meters and home to endemic and migratory endangered 
and vulnerable species.  It is roughly 5.1 km long, 3.2 km 
wide, and 167 m deep home to endemic and migratory 
endangered and vulnerable species (DNPWC, 2021). This 
region has an average annual rainfall of  around 462mm 
with an annual yearly temperature is about 11°C with a 
minimum of  4°C and a maximum of  27°C (RNP, 2019). 



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Imagery and Data
The Landsat ETM+ (Enhanced Thematic Mapper) 
images and the SRTM digital elevation model of  30m 
spatial resolution of  our research area were downloaded 
from the United States Geological Survey (USGS) data 
portal. The Landsat ETM+ images contain eight spectral 
bands with 30 meters of  spatial resolution for bands 
1 to 7. The panchromatic Band 8 features a 15-meter 
resolution. According to (Liu et al., 2005), Landsat images 
with a 30 meter spatial resolution are precise enough for 
mapping at a scale of  1:100,000. Table 1 shows the details 
of  satellite images used in this research.

Preprocessing (Periodic Line dropout, Radiometric 
and Atmospheric Correction) 
Periodic line dropouts may occur due to recording issues 
when one of  the detectors in the sensor either provides 
incorrect data or ceases to function. The Landsat 
Toolbox’s fix Landsat 7 Scanline Errors tool in ArcGIS 
(version 10.8.2) was employed to address these periodic 
line dropouts in satellite images. Notably, the Landsat-7 
image of  2000 did not contain dropouts, so only 
correction for periodic line dropouts in the Landsat-7 
images of  2010 and 2020 was performed.
Radiometric correction involves deducting the background 
signal (bias) and dividing by the instrument’s gain, 
thereby converting the raw instrument output (in DN) 
into radiance. This correction was utilized to convert the 
digital numbers (DN) of  satellite images into both spectral 
radiance (measured at the sensor) and reflectance.
Following the removal of  periodic line dropouts, 
radiometric correction was applied to multi-temporal 
Landsat 7 images of  2000, 2010, and 2020. Subsequently, 
atmospheric correction was performed post-radiometric 
correction. The aim of  atmospheric correction is to rescale 
the raw radiance data captured by the sensor and convert it 
into the surface reflectance values to remove atmospheric 
effects (Hadjimitsis et al., 2010; Tempfli et al., 2009).

Index Based Classification (NDVI, NDWI, TWI, Slope) 
These indices have proved to have significant utility in 
wetland mapping and change analysis across numerous 

Figure 1: Location map of  study area

Table 1: Landsat images used in this research
Image Path/Row Date of  Acquisition
Landsat 7 ETM+ 143/040 2000-12-22
Landsat 7 ETM+ 143/040 2010-12-18
Landsat 7 ETM+ 143/040 2020-12-29

Methodology
Before extracting wetlands, image preprocessing steps 
were undertaken for all Multi-temporal Landsat images 
using the ArcGIS software (version 10.8.2) package. 
These steps included periodic line dropout, radiometric 
corrections, and atmospheric correction. Furthermore, to 
derive slope in degree, TWI, NDVI, NDWI, supervised 
classification, and accuracy assessment were performed 
using ArcGIS software (Version 10.8.2).



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wetland studies. Incorporating NDWI and NDVI into 
classification processes resulted in improved outcomes 
and enhanced distinction of  wetland classes, making them 
widely adopted in wetland delineation efforts(Amani et 
al., 2021; Ashok et al., 2021; Behera et al., 2012; Dong et 
al., 2014; Mao et al., 2020; Wu, 2018).

Normalized Difference Vegetation Index
The red band (0.63 µm to 0.69 µm) and near-infrared 

band (NIR, 0.76 µm to 0.90 µm) are used to calculate the 
Normalized Difference Vegetation Index (Tucker, 1979). 
It is the most used index for monitoring change detection 
of  wetland. For analysis of  Landsat 7 images, NDVI of  
respective year images were derived using Band 3 (red) 
and Band 4 (NIR) (Figure 2). 
The formula for calculation is: 
NDVI=(NIR-RED)/(NIR+RED)             (2.1)

Figure 2: NDVI map for the years of  2000, 2010 and 2020

NDVI values range from -1 to 1, with zero representing 
bare soil, negative values indicating non-vegetated areas, 
and positive values signifying vegetation.

Normalized Difference Water Index
Green band (0.52 µm to 0.60 µm) and near infrared 
(NIR, 0.76 µm to 0.90 µm) bands are used to calculate 

the NDWI (McFEETERS, 1996). NDWI is employed 
to identify open water elements and accentuate their 
visibility in remotely captured digital images, while also 
filtering out soil and land vegetation features (Figure 3). 
The formula for calculating NDWI is: 
NDWI=(GREEN-NIR)/(GREEN+NIR)               (2.2)

Figure 3: NDWI map for the years of  2000, 2010 and 2020



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In Landsat 7 images, band 2 and band 4 denote the 
green and near-infrared portion of  the electromagnetic 
spectrum respectively. The value of  NDWI also ranges 
from -1 to +1, where negative value indicates vegetation 
and soil and therefore suppressed, while positive value 
indicate high vegetation water content thus are enhanced. 

Topographic Wetness Index (TWI)
Besides utilizing NDWI and NDVI as indicators of  
wetlands, topographic position can serve as an additional 

indicator of  wetland presence, constituting a commonly 
employed approach for detecting and mapping wetlands 
(Wu, 2018). The TWI is developed by (Beven & Kirkby, 
1979) within the runoff  model TOP-MODEL. It is 
computed for each pixel utilizing factors such as drainage 
area and slope.
Topography Wetness Index (TWI) is expressed as: 
TWI=ln(a/tan(b) )            (2.3)
Where a is the local up-slope contributing area draining a 
point per unit contour length and tan b is the local slope.

Figure 4:  Topographic Wetness Index Map of  Study Area

Figure 4 shows the TWI map of  the study area where 
the possible wetland zone is identified as the area with 
higher potential for water accumulation. TWI assesses 
a grid cell’s tendency to receive and keep water, with 
higher TWI values indicating greater water accumulation 
potential in those pixels (Besnard et al., 2013; Sørensen et 
al., 2006).

Slope 
Figure 5 shows the reclassified slope map of  the study 
area with slopes less than 5% was considered the potential 
wetland area, as many research studies used this threshold 
in identifying wetland areas (Berhanu et al., 2021; Furlan 
et al., 2021; Ozesmi & Bauer, 2002; Zhang et al., 2022; 
Zheng et al., 2017). 

Supervised Classification
A supervised classification algorithm typically comprises 
two phases: the learning or “training” phase, where the 
algorithm establishes a classification scheme using spectral 
signatures from “training” sites with known class labels. 
Generally, as the size of  training samples increases, the 
classification accuracy also rises accordingly, indicating 
a positive correlation between accuracy and the extent 
of  the training sample region to capture geographical 
variation (Ma et al., 2017). The second phase is prediction 
phase, where trained classifier is applied to locations with 
unknown class membership (Samaniego & Schulz, 2009).
Currently, among supervised classification methods for 
remotely sensed data, the maximum-likelihood classifier 
stands out as the most widely recognized and utilized. 



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This is attributed to its statistical robustness, simple and 
straightforward applicability, and the ease of  interpreting 
the obtained results (ERDAS (Firm), 1997; Lu & Weng, 
2007). Because of  this advantages and performance 
maximum likelihood is most commonly used supervised 
classification technique for wetland mapping (Forgette & 
Shuey, 2018; Ghobadi et al., 2012; Gosselin et al., 2014; 
Rebelo et al., 2009).
The maximum likelihood classifier, a parametric 
supervised classification method, operates on the 
principle of  assigning a pixel to a specific class based on 
the probability of  it belonging to that class. The basic 
equation supposes that these probabilities are equal 
for all classes, and that the input bands have normal 
distributions (ERDAS (Firm), 1997). It uses the training 
data as a means of  estimating means and variances of  the 
classes which are then used to estimate the probability. It 
considers both the variability of  brightness values within 
each class and average value for classification, unlike 
minimum distance or parallelepiped (Campbell & Wynne, 
2011; Ozesmi & Bauer, 2002). Then the multi-temporal 
Landsat images of  the research area were utilized for 
land cover classification into five distinct classes: Class 
1, designated as Forest; Class 2, representing Agriculture 
land; Class 3, denoting Grassland; Class 4, designated as 
Wetland; and Class 5, representing Barren land.

Accuracy Assessment 
An accuracy assessment was carried out on classified 
multi-temporal Landsat imagery, in which entire images 
were divided into training and testing sets, with 70% 
allocated for training the maximum likelihood classifier. 
The selection of  training samples involved visually 
interpreting the imagery and overlaying the final output 
of  index-based classifications, such as NDVI and NDWI. 
Additionally, derived slope and TWI from the SRTM 
DEM were used to assist in land cover classification as a 
complementary data.  Subsequently, the remaining 30% 
of  labelled samples were utilized to evaluate the accuracy 
and reliability of  the trained classifier. 
To determine the overall accuracy of  supervised 
classification result for year the years 2000, 2010, and 2020, 
a comprehensive examination employing a confusion 
matrix is conducted. This analytical tool facilitates a 
comprehensive comparison of  classification outcomes 
with predefined test site regions of  interest, providing 
key metrics such as user accuracy (UA), overall accuracy 
(OA), Kappa coefficient (KC), producer accuracy (PA), 
omission error and commission error.

RESULT AND DISCUSSION
Classification 
Land cover maps for the years 2000, 2010, and 2020 were 

Figure 5: Reclassified Slope Map of  Study Area



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derived using ArcGIS (version 10.8.2), shown in Figure 
6, using the above discussed Methodology. The study 
area’s land cover types were forest, grassland, agriculture 

land, barren land, and wetland. The study area showed 
significant changes in its land cover types from 2000 to 
2020.

Figure 6: Land cover classifications maps for the year 2000, 2010 and 2020

Figure 7 provides a detailed comparison of  the status 
of  these land cover types from 2000 to 2020. 53 % of  
the study area land was covered by the forest in 2000. 
This coverage was decline to 51.75% by 2010, but it again 
increased in 2020 to 52.39%. In 2000, grassland covered 
18.11% area of  the study area, which was increased to 
23.53% by 2010 and again decreased to 21.41% in 2020. 

The area of  agricultural land was decreasing over the 
years, from 18.02% in 2000 to 15.43 % in 2010 and 13.90 
% in 2020. Barren Land also varied, going from 7.64% 
in 2000 to 6.08% in 2010 before increasing to 9.10% in 
2020. The wetland area is also decreasing in a consistent 
trend with 3.24% coverage in 2000, 3.22% in 2010, and 
3.20% in 2020.

Figure 7: Status of  Landcover in Study Area

Overall Accuracy Assessment
Additionally, an evaluation of  the classification accuracy 
of  the land cover maps derived from Landsat imagery 
was conducted. Table 2, Table 3, Table 4 displays metrics 
such as user accuracy (UA), producer accuracy (PA), 
overall accuracy (OA) and Kappa coefficient (KC) for 
the classified maps of  2000, 2010, and 2020 respectively. 

The results indicate that both the overall accuracy and 
class accuracy (PAs and UAs) exceeded 85% and 80%, 
respectively, across all maps. Moreover, the Kappa 
coefficients surpassed 0.81, signifying as almost perfect 
agreement between the interpreted data and the reference 
data (McHugh, 2012).



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Spatial Distribution and Wetland Change
The analysis indicates a significant reduction in wetland 
areas within the study region over the past two decades. 
The wetland area was 1556.24 hectares in 2000, which 

decreased to 1545.89 hectares in 2010 and further to 
1536.71 hectares in 2020 (Figure 8). The overall decrease 
in wetland area was 1.255% from 2000 to 2020, with an 
annual degradation rate of  0.977 hectares.

Table 2: Accuracy assessment of  land cover classification for 2000
Land Cover User accuracy Producer accuracy Overall Accuracy Kappa coefficient
Forest 90.31 92.13

87.686 0.831
Agriculture land 80.59 82.59
Grassland 81.78 81.41
Wetland 91.68 92.30
Barren Land 92.01 88.25

Table 3: Accuracy assessment of  land cover classification for 2010
Land Cover User accuracy Producer accuracy Overall Accuracy Kappa coefficient
Forest 90.10 91.02

88.626 0.845
Agriculture land 81.36 88.72
Grassland 85.66 86.04
Wetland 94.80 91.19
Barren Land 93.40 87.41

Table 4: Accuracy assessment of  land cover classification for 2020
Land Cover User accuracy Producer accuracy Overall Accuracy Kappa coefficient
Forest 94.73 90.19

90.371 0.867
Agriculture land 85.43 90.15
Grassland 80.18 91.67
Wetland 94.62 92.30
Barren Land 94.45 89.69

Figure 8: Wetland Degradation graph for the Years 2000, 2010, and 2020

As research done by (Zhang et al., 2022) using Landsat 
images shows that, due to climate factors such as 
temperature and evaporation increase, change in 
precipitation, surface drought, and decreased vegetation 
coverage, and anthropogenic region, the Maqu wetland 
area decreased by 23.35% in total, about 261.19 km² 
from 1999 to 2020. Similarly, the Hokersar wetland has 
experienced the area degradation from 18.75 km² to 13 
km² between 1969 and 2008, marking a loss of  5.75 km² 
over the last four decades(Romshoo & Rashid, 2014); 
this decline is primarily due to nutrient and silt load from 

deforestation, pesticide and fertilizer use, encroachment, 
unplanned urbanization, and reduced discharge linked to 
climate change. 
The key drivers of  wetland degradation and loss are 
both natural and anthropogenic, which threaten the 
communities dependent on wetland-based livelihoods. The 
anthropogenic drivers include infrastructure development, 
technology use, excessive fertilizer and pesticide use, 
overexploitation of  wetland resources, and agricultural 
land expansion, whereas natural drivers include climate 
changes and natural disaster (Bhatta et al., 2018).



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The major threats to the high-altitude wetlands are 
degradation of  the catchment area, unplanned and 
irregular tourism, development activities, and immense 
grazing pressure (Gujja, 2007). To mitigate the impact 
and the threats, effective management strategies and 
wetland management adaptation practices to address the 
climate changing conditions are crucial to preserve the 
wetland ecosystem services (Gopal et al., 2010). 

Geographic Information System, Remote Sensing 
and Landsat 7 Images
Geographic information system, when combined with 
remote sensing utilizing the Landsat 7 ETM+ images, 
have been effective and accurate tools for mapping 
wetland areas, monitoring changes, and conducting 
degradation analysis as demonstrated in various studies 
(Ghobadi et al., 2012; Rebelo et al., 2009; Teferi et al., 
2010). This research utilized Landsat 7 imageries of  30 
meters spatial resolution, from which all the GIS layers 
were created. It has produced excellent results with good 
accuracy; it still presents challenges in classifying different 
areas more finely, such as distinguishing between barren 
and agricultural land or wetlands and other hydric soils. 
Using a photogrammetry approach (aerial photography) 
can enhance classification accuracy as it helps in 
distinguishing different land cover classes, especially if  
the imageries are captured more frequently during the 
leaf-off  season and on cloud-free days.  It reduces errors 
of  omission and commission, particularly those arising 
from wetlands covered by tree canopies and vegetation 
present during the leaf-on season (Stubbs et al., 2020). 
However, while photogrammetry is suitable for short-
term changes and smaller geographic areas, it is not 
feasible for research like ours, which spans a longer 
time and covers larger geographic areas, due to the 
inaccessibility of  historical data. In such cases, the 
fusion of  Landsat and SAR data can be utilize which 
will improves the delineation of  field boundaries and 
radiometric differences, reducing confusion in the 
classification of  natural vegetation with enhancing the 
classification accuracy (Castañeda & Ducrot, 2009).
This research is carried out using secondary data without 
site verification and validation. Integrating collected field 
data with multispectral imagery would help in accurately 
delineating land cover classes and wetland boundaries. 
Although this method produced precise and fine results, 
field data is still a limitation. It is also difficult to carried 
out field survey in high-altitude wetland due to budget 
and time required, accessibility, and human constraints. In 
contrast, satellite remote sensing proves highly beneficial 
for wetland inventories and monitoring in developing 
countries with limited funding and sparse.

CONCLUSION
This research has effectively mapped and analysed change 
detection in the high-altitude Rara wetland using remote 
sensing and GIS technologies along with multispectral 
Land 7 ETM+ images. The change in wetland extent has 

been determined using spectral indices NDVI and NDWI 
for different years, TWI, slope derived from DEM, and 
supervised classification for different years. For the past 
20 years, 1.25% of  the total area of  Rara wetland has 
been lost with a rate of  0.997 hectares annually. 
For the years 2000, 2010, and 2020, the overall 
classification accuracy and Kappa coefficient are above 
85% and 0.80 respectively. This accuracy highlights 
the reliability of  remote and GIS for wetland mapping 
and monitoring. In addition to being cost-effective and 
efficient, this approach offers a replicable method for 
analysing and detecting high-altitude wetland changes 
without carrying out extensive field surveys. 
The findings presented in this research have major 
implications for the conservation of  high-altitude 
wetlands and their ecological functions, such as water and 
air purification, habitat provision for endangered species, 
flood protection, biodiversity conservation, preventing 
shoreline erosion, and water storage. This highlights 
the urgent need to carry out similar research across all 
protected high-altitude wetlands and natural wetland areas 
to monitor changes and undertake restoration efforts.
Our research underscores the urgent necessity to 
safeguard Rara wetland from further degradation, given 
the alarming rate of  decline. So, decision-makers and 
concerned authorities must prioritize the implementation 
of  protective strategies to preserve the integrity and 
functionality of  high-altitude wetlands like Rara wetland.

Acknowledgements
The authors thank United States Geological Survey 
(USGS) for providing the Landsat 7 ETM+ imageries 
and SRTM DEM. We extend our gratitude to the 
reviewers for their critical observations and to everyone 
who contributed to the improvement of  this paper.

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