Resource Use and Resource Economy

A Comparison of Local- and Foreign-funded Hydropower Station Construction in Nepal based on Remote Sensing

  • TIMSINA Ritu Raj , 1, 2 ,
  • WU Mingquan , 1, 2, * ,
  • NIU Zheng 1, 2
  • 1. State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China
  • 2. University of Chinese Academy of Sciences, Beijing 100049, China
* WU Mingquan, E-mail:

TIMSINA Ritu Raj, E-mail:

Received date: 2021-10-10

  Accepted date: 2022-01-20

  Online published: 2022-10-12

Supported by

The Strategic Priority Research Program of Chinese Academy of Sciences(XDA19030304)


International investors in large infrastructure projects face numerous risks. To explore this issue, this paper compares the development of two hydropower stations in Rasuwa District, Nepal: Upper Trishuli 3A, which is fully funded by a Chinese government bank, and Rasuwagadhi, which is fully funded by local government banks. The construction of these two plants was compared between 2012 and 2020 using a visual interpretation method to extract data on roads, buildings, dams, and vehicles from 1-m-resolution remote sensing imagery. Two methods were used to compare the environmental impacts of each plant. Landsat 7/8 30-m imagery was used to monitor changes in the normalized difference vegetation index around the Upper Trishuli 3A hydropower station from 2012 to 2020 and around the Rasuwagadhi hydropower station from 2014 to 2020. Then, 1-m-resolution imagery was used to observe land-cover differences in these areas and time periods. The results indicate that: (1) despite various challenges, such as geological disasters, the COVID-19 pandemic, and a blockade by the Indian government, there was no difference in construction progress between the two hydropower stations. (2) The Upper Trishuli 3A Hydropower Station was associated with better environmental protection work, as there were continuous declines in vegetation growth near Rasuwagadhi and increased overall vegetation growth near Upper Trishuli 3A. (3) Energy projects funded by the Belt and Road Initiative have benefited developing countries enormously. Finally, local conditions should be thoroughly investigated during the construction of foreign-funded power stations.

Cite this article

TIMSINA Ritu Raj , WU Mingquan , NIU Zheng . A Comparison of Local- and Foreign-funded Hydropower Station Construction in Nepal based on Remote Sensing[J]. Journal of Resources and Ecology, 2022 , 13(6) : 1009 -1021 . DOI: 10.5814/j.issn.1674-764x.2022.06.006

1 Introduction

Hydropower is one of the earliest forms of large-scale electricity generation and remains one of the world's main energy sources. Rivers with high hydroelectric potential have been used since the 19th century and saw significantly increased use in the 20th century. By the 1950s, more than 5000 dams had been constructed around the world (Pandit and Grumbine, 2012). In countries with abundant water resources, hydropower stations are the main energy sources. For example, more than 90% of the electrical energy used in Nepal is generated by hydropower. With the global climate crisis receiving increasing attention, there has been renewed interest in hydropower as a source of clean energy. Because the construction of large-scale hydropower stations requires significant funds and a high level of technical expertise, such projects are considered major milestones in any country, especially developing ones. Nepal, a developing country in South Asia, has gradually increased its number of hydropower projects in recent years. Most of Nepal's hydropower stations are the run-of-river type due to its topography, which features mountainous terrain with many fast-flowing rivers.
The construction of a hydropower station is a complex undertaking that requires consideration of many issues, such as the environment, resettlement of local residents, prevention of geological disasters, and protection of cultural relics. Remote sensing is a new technology that has been widely used to find solutions to these problems. Recently, mega- dams were constructed to produce large amounts of hydroelectric energy. However, these dams have required river diversions that have had negative impacts on riverine ecosystems (Dynesius and Nilsson, 1994) and caused habitat loss for vulnerable animal species (Richter et al., 2003). Remote sensing technology can provide ground-level data that helps to study these environmental issues. Large-scale remote sensing applications, such as Terra/Aqua MODIS, the Landsat series, IKONOS, and Sentinel I, II, and III, can be used for environmental monitoring, which facilitates ecological protection and helps mitigate environmental emergencies (Zhao et al., 2014). According to Zhao et al. (2014), remote sensing has been used in China for regional ecological environmental monitoring, including for giant panda surveys and governance of loess plateaus. The wide applicability of remote sensing allows it to be used in multiple fields, and its variety of spatial and temporal scales allows its realistic, efficient, and unbiased use in ecological monitoring.
Furthermore, dams are considered to provide water sources for agricultural, domestic, and industrial use (Ajibade et al., 2020). To reduce the impact of dam construction on the environment and nearby regions, proper site selection is an important first step. It is necessary for decision-makers to consider various factors when selecting a site, which can be time-consuming. As such, the use of multispectral remote sensing data can help streamline this process (Abushandi and Alatawi, 2015; Jozaghi et al., 2018; Noori et al., 2019). Over the past few decades, researchers have made widespread use of satellite imagery and data to understand various hydrological variables, and used Geographic Information System (GIS) techniques to determine suitable sites for hydropower plants (Liu et al., 2003; Forzieri et al., 2008; Abushandi and Alatawi, 2015).
Most hydropower projects in Nepal lie in remote regions at high elevations. As such, natural disasters such as earthquakes, landslides, and glacier lake outburst floods (GLOFs) are unavoidable. These natural disasters cannot be completely controlled but their effects can be mitigated with the use of remote sensing data. The speed and cost-effectiveness of satellite image data can help minimize the effects of natural disasters, both before and after they occur (Kaku and Held, 2013). Satellite sensors such as MODIS, Aster, Landsat, and Radarsat are used to produce hazard and disaster-risk assessment maps. Similarly, LIDAR, SRTM, and DEM can be used for flood prediction and hydrological modeling. The GOES satellite multispectral sensors are used for numerical weather prediction and to predict hydrological and hydrogeological risks (Roth et al., 1996).
In addition to the potential environmental impacts of hydroelectric dam construction, the relocation of citizens is another important issue that must be addressed. Nepal has minimal resettlement cases in this context. Resettlement issues can include physical displacement, economic problems arising after relocation, and the acquisition of new residential land. The Upper Trishuli project required the resettlement of 154 households with corresponding compensation for their land (Soeftestad et al., 2021).
Hydropower projects can also affect cultural heritage sites. For example, the Trishuli River Basin features in many Hindu cultural practices, such as cremation and ceremonial rites. The river is also an important pathway for pilgrims and tourists visiting the temple in the basin (International Finance Corporation, 2018). Remote sensing is a powerful tool for gathering information related to cultural sites based on multispectral, hyperspectral, and synthetic aperture radar imagery. Before a site is chosen for a hydropower project, satellite imagery can detect cultural sites and historical relics associated with the river basin and classify their features, such as their size and the groups with which they are associated (Mao et al., 2008; Lu et al., 2012).
Remote sensing today is a widely used technology for environmental monitoring. It informs many sectors of research with descriptive information about specific districts and remote villages (Markogianni and Dimitriou, 2016). Land-cover and vegetation monitoring have been made possible with remote sensing data such as the normalized difference vegetation index (NDVI), which many studies have used to assess vegetation (Hielkema et al., 1986; Zhang et al., 2003; Cheret and Denux, 2011).
The construction of large hydropower stations requires significant amounts of technology and capital. This can be challenging for developing countries, especially for the least-developed countries like Nepal. Therefore, seeking technical and financial support from other countries is an important way that developing countries can gain access to hydropower. After the “Belt and Road” Initiative was put forward, foreign investment has increasingly become one of the main pathways to large-scale hydropower construction in developing countries. The novel problems that may be encountered during hydropower station construction in foreign countries are an important concern for international investors.
To explore this issue, this paper uses remote sensing technology to monitor and compare the construction and associated ecological environmental impacts of local- and foreign-built power stations. The impacts of geological disasters, raw material procurement, and employment on these projects are then analyzed comparatively.

2 Study area and data preprocessing

2.1 Study area

The local and foreign power stations studied in this paper were the Rasuwagadhi and Upper Trishuli 3A hydropower stations, which are located in the Rasuwa District of Nepal. Rasuwa District lies in the Himalayan region 120 km north of the capital city of Kathmandu and is the smallest of the 17 Himalayan districts. The northern part of the district is connected to Tibet, an autonomous region of China. The district has a total area of 1512 km2, comprising 1.03% of the total area of Nepal. The study region lies in the Trishuli River basin, of which more than 60% lies in Tibet. Snow and glaciers are the greatest water sources for the river. More than 11% of the catchment area is above 6000 m in elevation. The precipitation in this district is highest in August (560.85 mm) and lowest in January (33.70 mm). Six other major hydropower plants operate on the Trishuli River.

2.1.1 Upper Trishuli 3A hydropower station

The Upper Trishuli 3A hydropower plant is located in Thulogaun, Uttargaya Rural Municipality, Rasuwa District, 95 km north of Kathmandu at 28°01'31"N, 85°11'09"E, at elevations of 765-853 m above sea level. The nearest villages are Archale (1.18 km away), Fikuri (2.58 km) and Dandagaun (3.12 km). The bazaar (market) in the main city of Trishuli is 16.3 km to the southeast, and the main headworks of the Upper-Trishuli 3A hydropower station are 5.5 km north of the plant on unnamed roads. The installed capacity of the plant is 60 MW and its annual generation power was forecast to reach 489.76 GWh, which is the country's second-highest annual amount of generation after the Kaligandaki A hydropower station. The region is mainly covered by dense forest. Maize and rice are the major products of this area and no major roads connect to the study area. Agricultural land containing rice paddies and maize fields was significantly reshaped after the initial construction of the plant in June 2011 and 2012. The hydroelectric plant is a run-of-river-type plant that can generate 75% of its capacity even in the dry seasons. For eight months of the year, it can run at its full capacity of 60 MW and, for the remaining four months, it has a power generation capability of 45 MW. Overall, the plant fulfills 7% of the total energy demands of Nepal. The project began in April 2011 but, due to various factors, such as the 2015 Nepal earthquake and disputes with local residents, it was halted for 22 months. This hydropower project was funded by the Export-Import Bank of China for a total cost of US$114.7 million at an interest rate of 1.75% per year, with a repayment period spanning 20 years from the date of commercial operation. Satellite images of the Upper Trishuli 3A hydropower station in 2015 and 2020 are shown in Fig. 1.
Fig. 1 Upper Trishuli 3A hydropower station in October 2015 (A) and October 2020 (B)

2.1.2 Rasuwagadhi hydropower station

Located in the town of Timure of Gosaikunda Rural Municipality 2, the Rasuwagadhi hydropower station is situated 150 km north of Kathmandu at 28°15'14"N, 85°22'36"E and elevations of 1524-1830 m above sea level. The nearest village, Piding, is 1.3 km away, Timure's main market is 2.6 km away, and the village of Thuman is 3.5 km away. Like the Upper Trishuli plant, it is also a run-of-river-type hydropower station and has an installed capacity of 111 MW and an annual generation capacity of 613.875 GWh. During the dry months, its energy production is 84.318 GWh, which is only 14% of its total energy production, while the remaining 529.557 GWh is generated during the wet season. The project is located 1.08 km from the Nepal-China border. It was built as a joint venture of the Independent Power Producers (IPP) of Nepal and the Nepal Electricity Authority (NEA) for a total cost of 1.36842×1010 Nepali Rupees (NRs). The Himalayas in the northwestern part of this area mean that the area is largely covered in snow. Most of its greenery was lost after the project had adverse effects on the land (mainly cattle grazing areas) and forest (Himalayan alpine trees and bushes). The hydropower project began in February 2014 and was targeted for completion in February 2020; however, due to dry landslides on June 20, 2019, the road leading to the site was heavily damaged. It was similarly affected by the 2015 Nepal earthquake, the Indian blockade, and seasonal floods. Figure 2 shows satellite images of the construction of the Rasuwagadhi hydropower station.
Fig. 2 Satellite images of Rasuwagadhi hydropower station in (A) November 2014 and (B) January 2020

2.2 Data and data preprocessing

2.2.1 Landsat data

Landsat satellite imagery was used in this research to identify annual changes in the vegetation of the areas surrounding both hydropower stations. Two Landsat satellite sensors were used: the Landsat 8 Operational Land Imager (OLI) and the Landsat 7 Enhanced Thematic Mapper (ETM+) at 30 m resolution. Three Landsat 8 GeoTIFF images and one Landsat 7 GeoTIFF image with < 4% cloud were taken annually from 2014 to 2020 and from 2012 to 2020, respectively. All satellite imagery was obtained from the USGS Earth Explorer website ( This Landsat imagery was used to monitor changes in vegetation cover after the start of construction of the hydropower station in each study area. Landsat 7 sensor imagery usually comes with scale line errors, so these errors were removed using the Landsat_gapfilter.sav plug-in in ENVI software. Similarly, all Landsat data were preprocessed in ENVI 5.3 software, which included image clipping and radiometric and atmospheric corrections. The Landsat imagery used in this research paper is summarized in Table 1.
Table 1 Landsat data used in this study
Satellite Sensor Path/Row Date Resolution (m)
Landsat-8 OLI 141/40 2020-12-07 30
141/40 2014-12-07 30
141/41 2020-12-07 30
Landsat-7 ETM+ 141/41 2012-12-07 30

2.2.2 High-spatial-resolution Google Earth images

High-resolution images downloaded from the Google Earth online application were used to examine the land use and land cover patterns surrounding the two hydropower stations each year. The images were acquired from 2012 to 2020 for the Upper Trishuli 3A hydropower station site, and from 2014 to 2020 for the Rasuwagadhi hydropower station site. The images were first clipped in ArcGIS so that all the extracted images had equal dimensions. Construction of the Upper Trishuli 3A hydropower station lasted eight years, whereas the Rasuwagadhi hydropower station was expected to be completed by February 2020 but was severely affected by the COVID-19 pandemic, thus postponing its completion date by one year. The patterns of land-cover change in both study areas were monitored using supervised classification and closely investigated in relation to the construction process of each hydropower station.

3 Method

To compare the differences in project construction progress and the environmental impact of local and foreign-built power stations, the Rasuwagadhi and Upper Trishuli 3A hydropower stations were monitored using remote sensing data. Their construction processes were compared on metrics including roads, buildings, dams, and vehicles, which were extracted from 1-m resolution image data spanning the period from 2012 to 2020 using a visual interpretation method. The environmental impacts of these two plants were compared using two methods. First, changes in NDVI around the Upper Trishuli 3A hydropower station were monitored from 2012 to 2020 and around the Rasuwagadhi hydropower station from 2014 to 2020 using 30-m Landsat 8/7 image data. Then, 1-m land-cover changes were monitored via visual interpretation using 1-m resolution image data around the Upper Trishuli 3A hydropower station from 2012 to 2020 and around the Rasuwagadhi hydropower station from 2014 to 2020. Construction process monitoring was conducted in the areas of these two plants. Comparisons were made within 2-km buffer zones around each site to show the environmental impacts of each plant. Figure 3 shows a flowchart of the research process used in this study.
Fig. 3 Flowchart of the research process used in this study

3.1 Construction progress monitoring

Google Earth images containing 1-m resolution data were used for monitoring of the construction process of the two hydropower stations. First, changes to the construction site, nearby houses, and dam area were identified by visual interpretation. Then, land-cover types were classified as barren land, agricultural land, forest area, water, and so on. Similarly, roads and vehicular movement were monitored from the construction phase until the hydropower station started generating power.
The process was monitored in two steps. Firstly, land-cover maps encompassing the study areas in different years were generated from Google Earth 1-m-resolution images via visual interpretation in ArcGIS software. Secondly, visualization of other factors that directly affect construction progress—e.g., the numbers of vehicles in the study areas, shifts in construction materials, and changes to buildings—was used to determine the progress in hydropower plant construction.

3.2 Environmental impact monitoring

3.2.1 Changes in vegetation index

In this research, the NDVI was used to measure changes in vegetation in the areas surrounding the two hydropower stations. To determine vegetation growth, multiple Landsat sensors were used, such as Landsat 8 OLI and Landsat 7 ETM+, and satellite imagery was obtained from USGS Earth Explorer. For the Upper Trishuli 3A hydropower station, the acquisition dates were December 7th, 2012 (Landsat 7) and December 7th, 2020 (Landsat 8). Similarly, for the Rasuwagadhi hydropower station, the data were acquired on December 7th, 2014 and December 7th, 2020 using the Landsat 8 OLI sensor. These data were first used to determine actual radiance values after radiometric correction and to produce final vegetation coverage maps of the study regions using ENVI 5.3 software. The data were also used to produce statistics on each category of vegetation, starting with low-moderate-high vegetation reflectance using ArcGIS 10.5 software. The following equation was used to estimate the NDVI (Markogianni and Dimitriou, 2016)
where NIR and R refer to the near-infrared band (Band 5: 0.85-0.88 µm) and red band (Band 4: 0.64-0.67 µm), respectively, for Landsat 8 data. Similarly, to determine the vegetation growth sequence from Landsat 7 data, Band 4 (0.77-0.90 µm) and Band 3 (0.63-0.69 µm) were used. Equation 1 was used for both sensors' data. The obtained satellite imagery was transformed from the WGS 1984 datum to WGS 1984 UTM Zone 45N (Rokni and Musa, 2019).

3.2.2 Land-cover and land-use changes

Changes in land cover are an important aspect of this research, as the study is based on the comparative analysis of land-cover dynamics in the vicinity of the two hydropower stations. We used high-resolution imagery as a basis for monitoring land-cover dynamics. For the Upper Trishuli 3A hydropower station study area, we selected images acquired during October 2012 and October 2020. For the Rasuwagadhi hydropower region, we selected images acquired during November 2014 and January 2020. These times were selected based on the stations' initial construction phases. The land types were similar in both regions; however, some cloud cover was present in the 2012 image of the Upper Trishuli 3A hydropower plant area, which was taken into consideration in a small portion of the analysis of the dynamics.
Land-cover maps around both stations were classified by visual interpretation methods using 1-m high resolution imagery downloaded from Google Earth. The land types were mainly classified as forest, water, houses, roads, and barren land. Field visits were not possible and, as such, these same images were used to validate our findings, and a ground-truth region of interest (ROI) was obtained from ENVI with more than 40 ROIs sample images per year. Post-classification was used to generate a confusion matrix of all ROIs to validate user accuracy.

4 Results

4.1 Comparative monitoring of construction progress

Table 2 shows the monitoring results in terms of the areas covered by roads, houses, construction and materials, the power station itself, and the dam, and numbers of vehicles around the Upper Trishuli 3A and Rasuwagadhi power stations from 2012 to 2020. In 2012, both study areas were mainly undeveloped countryside with few houses present. One main road (the Pasang-Lhamu Highway, also called Tibet Road) passed through these two areas. There were also some dirt roads present in the Upper Trishuli 3A area.
Table 2 Areal coverage of different structures (ha) and vehicle numbers around the two hydropower stations
Category Rasuwagadhi area Upper Trishuli 3A area
2012 2014 2017 2020 2012 2014 2017 2020
Roads 0.51 1.91 1.93 1.74 0.79 0.96 1.28 1.32
Construction and materials 19.75 35.10 6.38 37.23 34.33 38.70
Houses 0.50 14.83 32.65 33.40 2.69 7.63 9.27 7.01
Power station 19.59 18.93 23.07
Sand filtering area 17.24 18.39 11.32
Dam 0 13.37 35.73
Vehicles number 8 48 65 14 16 28
From 2012 to 2020, after the beginning of construction, the areas covered by roads, houses, construction and materials, the power station, and dam, and the numbers of vehicles increased at both sites (Table 2, Fig. 4 and Fig. 5). The area covered by houses at the Rasuwagadhi site increased from 0.50 ha to 32.65 ha, and the area covered by roads increased from 0.51 ha to 1.74 ha. Sand filtering areas were as important as roads during the construction process but, in 2020, the area they covered was reduced to make space for vehicles and accessories. A similar process occurred around the Upper Trishuli 3A power station. The detailed monitoring process is listed in Figures 4 and 5 and Table 2, and shows the changes in the number of vehicles present over the years.
Fig. 4 Constructed areas in the Upper Trishuli 3A power station region on (a) December 12, 2014, (b) December 27, 2017, and (c) October 25, 2020, showing construction progress.
Fig. 5 Constructed areas in the Rasuwagadhi hydropower region on (a) December 12, 2014, (b) November 30, 2017, and (c) January 9, 2020, showing construction progress.
In addition to the significant changes occurring near these hydropower stations, significant changes were also noted within the 2-km buffer zones, especially that surrounding the Upper Trishuli 3A station. Seven new villages with new roads and more than 100 houses appeared in this zone during the construction period.

4.2 Comparative monitoring of environmental impact

4.2.1 Monitoring impacts on vegetation status using NDVI

The NDVI was used to examine vegetation growth sequences at the two hydropower plant sites. It was analyzed in specific years to explore how it has been modified and its effect on the natural ecosystem. The NDVI was used to measure vegetation growth sequences in different areas to gain an understanding of the growth patterns in the rough, dry land in the upstream region of the Trishuli River and in the fertile land in the midstream regions. For the Upper Trishuli 3A area, Landsat 8 2020 and Landsat 7 2012 imagery was used and, for the Rasuwagadhi area, Landsat 8 2014 and 2020 data were used.
The Upper Trishuli 3A hydropower station area is covered by a mixture of dense vegetation and barren land with small bushes, which have high NDVI values for dense vegetation and moderate NDVI values for barren land throughout the year (Figure 6, Table 3). In contrast, the Rasuwagadhi power station area has snow-covered mountains in the northwestern and northeastern parts, as well as a very small forest area in the southern part. Therefore, this area is rough and dry and exhibits low to moderate NDVI values (Figure 7, Table 4).
Fig. 6 Distribution of NDVI near the Upper Trishuli 3A hydropower station in (a) 2012 and (b) 2020
Table 3 Upper Trishuli 3A hydropower station vegetation monitoring
NDVI range December 7, 2012 December 7, 2020
Percentage (%) Area
Percentage (%)
NDVI < -0.2 36.9 7.5 7.38 1.51
-0.2 ≤ NDVI < 0 106.56 21.68 46.26 9.41
0 ≤ NDVI < 0.2 244.08 49.67 199.98 40.69
0.2 ≤ NDVI < 0.4 103.68 21.09 221.85 45.14
0.4 ≤ NDVI < 0.6 0.18 0.03 15.93 3.24
Fig. 7 Distribution of NDVI near the Rasuwagadhi hydropower station in (a) 2014 and (b) 2020
Table 4 Rasuwagadhi hydropower station vegetation monitoring
NDVI range December 7, 2014 December 7, 2020
Percentage (%) Area
Percentage (%)
NDVI < -0.2 0.09 0.16 3.96 7.35
-0.2 ≤ NDVI < 0 1.26 2.34 18.27 33.94
0 ≤NDVI < 0.2 6.66 12.37 19.53 36.28
0.2 ≤ NDVI < 0.4 19.08 35.45 8.1 15.05
0.4 ≤ NDVI < 0.6 26.73 49.66 3.96 7.35

4.2.2 Land-cover change

The Upper Trishuli 3A region consists mainly of a forested area, bare land, shrubs, and agricultural land, whereas the Rasuwagadhi station is surrounded by bare land and large numbers of buildings. Figures 8 and 9 demonstrate the changes in land cover in the areas surrounding the two hydropower stations and the specific areas are listed in Table 5. The classified images produced satisfactory results, resulting in 85% overall accuracy, on average, for the two regions (Table 6).
Fig. 8 Land cover around the Upper Trishuli 3A hydropower station in October 2012 (a) and October 2020 (b)
Fig. 9 Land cover around the Rasuwagadhi hydropower station in November 2014 (a) and January 2020 (b)
Table 5 Land cover areas (ha) around the Upper Trishuli 3A and Rasuwagadhi hydropower stations in two years
Category Upper Trishuli 3A Rasuwagadhi
October, 2012 October,
November, 2014 January,
Water 120.41 101.37 78.87 57.98
Forest 265.74 316.38 224.40 218.67
Roads 0.647 2.43 1.246 1.42
Houses 2.35 13.30 11.29 22.33
Bare land 99.53 135.90 475.10 684.41
Agricultural land 227.34 129.06 0.00 0.00
Cloud cover 97.61 0.00 0.00 0.00
Bridge 0.00 0.00 0.015 0.028
Shrubs 438.61 536.59 475.30 305.83
Sandy area 80.31 101.83 64.98 63.40
Table 6 Accuracy evaluation of the land-cover maps of the Upper Trishuli 3A and Rasuwagadhi hydropower station regions
Category Upper Trishuli 3A Rasuwagadhi
Producer accuracy (%) User accuracy
Producer accuracy (%) User accuracy (%)
Water 100.00 97.72 72.09 81.00
Forest 83.87 96.30 100.00 100.00
Roads 85.18 91.27 82.00 100.00
Houses 85.16 97.48 87.00 83.98
Bare land 65.37 76.31 92.86 63.79
Agricultural land 70.23 83.20 - -
Cloud cover 100.00 100.00 - -
Shrubs 70.87 59.60 99.52 96.76
Sandy area 77.34 79.18 60.24 66.67
Sand filtering - - 90.02 92.74
Total 82.00 86.78 84.96 85.61
The Upper Ttrishuli 3A region experienced major changes to its bare land area, which increased by 36.37 ha (15%) between 2012 and 2020. The area covered by houses increased by 10.95 ha (nearly 60%), the water area decreased slightly by 8%, agricultural land decreased by 98.38 ha (38%), and forest and road networks were observed to increase by 8% and 57%, respectively. Similarly, shrub and sandy areas were also significant, with shrubland increasing by 97.98 ha (10%) and sandy areas increasing by 21.52 ha (11.81%).
Similarly, the Rasuwagadhi region saw increases in the areas of houses (11.04 ha, 32%), roads (0.174 ha, 6.5%), and bare land (209.31 ha, 18%). At the same time, there were decreases in areas of water (16.89 ha, 12%), shrubland (169.47, 21%), sandy areas (1.58 ha, 1.23%), and forest (5.73 ha, 1.29%). By 2020, the area taken up by the bridge moved slightly closer to the power station area to facilitate the transport of materials, but this did not affect the land cover.

5 Discussion

5.1 Factors affecting construction progress

Several factors can be identified as delaying the construction processes in the study regions, including the 2015 Nepal earthquake and resultant landslides, which affected the major highway connecting several power plants. During the 2015 earthquake, major hydropower stations—both under construction and completed—were severely damaged, including the two studied stations. Figure 10 (Huang and Liu, 2017; Pehlivan et al., 2017) shows damage to the Upper Trishuli 3A hydropower station, including 1) the dam and suspension bridge, which were damaged by falling rocks, 2) a power transmission line, which was hit by a falling rock, and 3) a main access road, which was blocked due to landslide debris around the power station. These factors delayed construction for nearly 22 months; the workers were regrouped in 2017.
Fig. 10 Major damage after the 2015 earthquake in the Upper Trishuli 3A power station region
In the Upper Trishuli 3A hydropower station region, rock-fall debris affected the main access road for up to 5 km. Although the rock here is mainly metamorphic bedrock, there is the potential for future damage due to loosen soil.
Compared with the Upper Trishuli 3A power plant, the Rasuwagadhi power plant is especially vulnerable to damage due to its geological location. Figure 11 (Huang and Liu, 2017; Pehlivan et al., 2017) shows damage to the Pasang- Lhamu Highway (the power station's main road), which was blocked by landslide debris from both sides of the road.
Fig. 11 Pasang-Lhamu Highway blocked by heavy landslides caused by the 2015 Nepal earthquake
The Rasuwagadhi region features rocky mountain faces on both sides of the power station consisting of gneissic and quartzitic bedrock. In addition, the fast-flowing Trishuli River can become blocked by falling rocks, which cause much damage in this region. During the 2015 earthquake, the bedrock was significantly loosened and a subsequent monsoon caused flooding in some parts of the power plant area. The remaining rocks and the overall roughness of this region's landscape make it vulnerable to future flooding and rock-fall damage.
However, other factors—such as seasonal floods, the COVID-19 pandemic, shortages of materials and manpower, and a blockade by the Indian government—must also be considered. The undeclared Indian blockade at this region's border majorly impacted the Nepalese energy and transportation sectors and economic development due to fuel and materials being stopped at border checkpoints. Although the Upper Trishuli 3A hydropower station had to deal with the earthquake and Indian embargo, it was able to produce electricity by June 2019, whereas the Rasuwagadhi hydropower station faced significantly greater challenges that halted its construction progress.
The outbreak of the COVID-19 pandemic caused a nationwide lockdown, which severely affected Nepal's ability to import construction materials. Furthermore, foreign workers were unable to return to the construction site due to the travel bans imposed by many countries, including China. There were 530 local workers and 160 Chinese workers working at the Rasuwagadhi site. Similarly, nearly 1600 people were hired to work on the Upper Trishuli 3A power plant from its initial development to its completion. The pandemic delayed the expected completion of 151 MW of hydropower plants in Myanmar, 510 MW of hydropower plants in Indonesia, and many other Nepalese power plants (Cox, 2020). Good planning is needed to minimize the damage caused by natural disasters during the construction process.

5.2 Factors affecting the ecological environment

Our research focused on how landscapes have been reshaped by the development of hydropower stations around the villages of Thulogaun and Timure in Rasuwa District.
The impacts of various factors on the environment are quantified using three parameters: NDVI, waste accumulation during the construction process, and reductions in deforestation. It should also be considered that these hydropower plants provide electricity that allows households to reduce wood burning for heating and cooking.
The areas with NDVI values > -0.2 or < 0.6 around the Rasuwagadhi power station had significantly changed. The areas with NDVI values higher than 0.4 significantly declined due to anthropogenic factors (Table 4). In comparison, the Upper Trishuli 3A power station had significantly increased NDVI values. Similarly, low NDVI values have been gradually reduced in the Upper Trishuli 3A area, whereas the area with low NDVI values around the Rasuwagadhi area has experienced increases with the development of houses, roads, and mining. Another factor likely to cause significant environmental change is waste accumulation during station development. However, the planning commissions of the two hydropower stations managed waste areas in a way such that they could be restored to their original states.
In some ways, the Upper Trishuli 3A power plant region has become more vulnerable to landslides due to roads being built to connect nearby villages. It can be seen from the Google Earth image that although there is a reinforced slope (Fig. 1), it is still at risk of landslide. On the other hand, there are two pieces of bare land next to a stream 2 km away from the plant, which may be temporary material handling sites for the construction site. These areas have not been afforested yet and may be at risk of soil erosion during the monsoon season.

5.3 Influences of foreign investment in power stations on local labor markets, technological progress, and other aspects of the social economy

Foreign direct investment has benefited Nepal in terms of overall development and new infrastructure. This study focuses on analyzing foreign investment in the hydropower sector and its wider impact. Multiple hydropower projects have been successfully established through foreign aid in Nepal. The United States, Norway, India, and China have all invested in Nepalese hydropower over the past few decades (Dhungel, 2016). The Upper Trishuli 3A power station was funded by a loan from the Exim Bank of China, which is payable in 25 years. With this project, the Uttargaya municipality in which the power station is located has been able to develop its industries, local market, and tourist attractions. The total number of people living in the Uttargaya municipality was 5490 in 2014 (Central Bureau of Statistics 2014), and 42 houses in five villages were affected by the power plant, of which 28 houses were physically impacted (18%). The government provided compensation of US$2085 to each owner of the affected houses (International Finance Corporation, n.d.).
The trade opportunities and increases in wages created by Chinese investment in the Upper Trishuli 3A power plant are essential factors in the overall development of the Uttargaya municipality. Sand and sedimentation mining and exportation to Kathmandu and local regions were major sources of money that created economic relationships between cities and the ability to export other goods, such as local vegetables and dairy products, thus greatly increasing trade opportunities. Local residents were hired for power plant construction, increasing their monthly wages and providing opportunities to skilled and semi-skilled people. Eight hundred local people were employed during the construction process, resulting in wages that were 20% higher than those provided by local farms. The construction of the Upper Trishuli 3A power plant, which lasted for almost 10 years, was also used to develop multiple sectors in the region, leading to expansion in the local market via the establishment of hotels, restaurants, and hospitals. The Chinese firm used their own technology and advanced equipment and, as such, local people were trained as professionals, which increased their standard of living. The construction of this power plant provided employment to nearly 1600 local people. During the construction of the two hydropower stations, roads and temporary houses were built in large numbers to accommodate the workers. The Rasuwagadhi power station led to a 57% increase in the construction of roads, compared to an increase of 47% in the Upper Trishuli area from 2012 to 2014. After 2017, road construction in the Rasuwagadhi area decreased as there was an increased need for temporary housing; while in the Upper Trishuli region, roads were continuously built to facilitate local trade with the nearest city. The temporary residences constructed in the Rasuwagadhi region increased in area by 32.9 ha from 2012 to 2020, whereas the Upper Trishuli region experienced a decrease in the housing area of 2.26 ha from 2017 to 2020, due to the demolition of temporary buildings.
This analysis demonstrates that transnational hydropower projects can be a good way to increase the electricity generation capacity of power projects in Nepal, which subsequently reduces the need to import electricity from other countries.

6 Conclusions

In order to determine the problems that foreign investors may encounter during the process of constructing hydropower stations, two such processes were compared in this paper based on remote sensing of the landscape. The main conclusions are as follows:
(1) There was no difference in project construction progress between power stations that had foreign or domestic investment. However, many factors affected these projects, such as geological disasters, the COVID-19 epidemic, and the blockade by the Indian government. During the construction of foreign-funded power stations, the local conditions should be fully investigated; however, the construction period cannot be reliably predicted based on local conditions.
(2) The Upper Trishuli 3A hydropower station was associated with more successful environmental protection measures than the Rasuwagadhi hydropower station. The land areas occupied by the two power stations are very small, and their construction waste was handled properly. However, the NDVI results show that the vegetation was less affected around the Upper Trishuli 3A hydropower station. The areas with NDVI values <0 around the Rasuwagadhi hydropower station increased by 38.79%, while they declined by 18.26% around the Upper Trishuli 3A hydropower station.
(3) The construction of the two power stations has promoted the development of the local economy. House and road numbers increased around both power stations; by 5.65- and 3.76-fold around the Upper Trishuli 3A hydropower station and by 1.98- and 1.14-fold around the Rasuwagadhi hydropower station. Overall, more houses and roads were built around Upper Trishuli 3A.
(4) The Belt and Road Initiative, in collaboration with the Nepalese government, has helped to increase trade opportunities and hydropower investment and provide clean electricity to the people of Nepal.
Abushandi E, Alatawi S. 2015. Dam site selection using remote sensing techniques and geographical information system to control flood events in Tabuk City. Hydrology Current Research, 6(1): 1000189. DOI: 10.4172/2157-7587.1000189.


Ajibade T F, Nwogwu N A, Ajibade F O, et al. 2020. Potential dam sites selection using integrated techniques of remote sensing and GIS in Imo State, Southeastern Nigeria. Sustainable Water Resources Management, 6(4): 57. DOI: 10.1007/s40899-020-00416-5.


Cheret V, Denux J P. 2011. Analysis of MODIS NDVI time series to calculate indicators of Mediterranean forest fire susceptibility. GIScience & Remote Sensing, 48(2): 171-194.

Dhungel K R. 2016. A history of FDI in hydropower development in Nepal. Hydro Nepal: Journal of Water, Energy and Environment, 18: 22-24. DOI: 10.3126/hn.v18i0.14639.


Dynesius M, Nilsson C. 1994. Fragmentation and flow regulation of river systems in the northern third of the world. Science, 266(5186): 753-762.


Forzieri G, Gardenti M, Caparrini F, et al. 2008. A methodology for the pre-selection of suitable sites for surface and underground small dams in arid areas: A case study in the region of Kidal, Mali. Physics and Chemistry of the Earth, Parts A/B/C, 33(1-2): 74-85.

Hielkema J U, Prince S D, Astle W L. 1986. Rainfall and vegetation monitoring in the savanna zone of the Democratic Republic of Sudan using the NOAA advanced very high resolution radiometer. International Journal of Remote Sensing, 7(11): 1499-1513.


Huang Y, Liu J L. 2017. Seismic performance of hydropower plant and highway system during the 2015 Gorkha earthquake in Nepal. Proceedings of the 16th World Conference on Earthquake Engineering, Santiago, Chile: 9-13.

IFC (International Finance Corporation). 2021. A new lifeline for west Africa's smaller enterprises. Viewed on June 10, 2021.

Jozaghi A, Alizadeh B, Hatami M, et al. 2018. A comparative study of the AHP and TOPSIS techniques for dam site selection using GIS: A case study of Sistan and Baluchestan Province, Iran. Geosciences, 8(12): 494. DOI: 10.20944/preprints201810.0773.v2.


Kaku K, Held A. 2013. Sentinel Asia: A space-based disaster management support system in the Asia-Pacific region. International Journal of Disaster Risk Reduction, 6: 1-17.


Li Y J, Zhang Y Q, Tiffany L A, et al. 2021. Synthesizing social and environmental sensing to monitor the impact of large-scale infrastructure development. Environmental Science & Policy, 124: 527-540.

Liu J, Chen J M, Cihlar J. 2003. Mapping evapotranspiration based on remote sensing: An application to Canada's landmass. Water Resources Research, 39(7). DOI: 10.1029/2002WR001680.


Lu X, Hou M L, Hu Y G. 2012. The application of hyper-spectral remote sensing in cultural relic conservation. Advanced Materials Research, 446-449: 3798-3802.


Mao F, Liu Z, Zhou W S, et al. 2008. Research and application of spatial information technology on Grand Canal of China. IEEE International Geoscience and Remote Sensing Symposium, III-1300-III-1303. Boston, USA.

Markogianni V, Dimitriou E. 2016. Landuse and NDVI change analysis of Sperchios River Basin (Greece) with different spatial resolution sensor data by Landsat/MSS/TM and OLI. Desalination and Water Treatment, 57(60): 29092-29103.


Noori A M, Pradhan B, Ajaj Q M. 2019. Dam site suitability assessment at the Greater Zab River in northern Iraq using remote sensing data and GIS. Journal of Hydrology, 574: 964-979.


Pandit M K, Grumbine R E. 2012. Potential effects of ongoing and proposed hydropower development on terrestrial biological diversity in the Indian Himalaya. Conservation Biology, 26(6): 1061-1071.


Pehlivan M, Madugo C M, MacDonald A, et al. 2017. Performance of hydropower infrastructure after the 2015 Gorkha Earthquake sequence. Earthquake Spectra, 33(S1): 115-132.


Qu Y, Zheng Y, Gong P, et al. 2022. Estimation of wetland biodiversity based on the hydrological patterns and connectivity and its potential application in change detection and monitoring: A case study of the Sanjiang Plain, China. Science of the Total Environment, 805: 150291. DOI: 10.1016/j.scitotenv.2021.150291.


Richter B D, Mathews R, Harrison D L, et al. 2003. Ecologically sustainable water management: Managing River flows for ecological integrity. Ecological Applications, 13(1): 206-224.


Rokni K, Musa T A. 2019. Normalized difference vegetation change index: A technique for detecting vegetation changes using Landsat imagery. CATENA, 178: 59-63.


Roth G, Barrett E, Giuli D, et al. 1996. The storm project: Aims, objectives and organisation. Remote Sensing Reviews, 14(1-3): 23-50.


Soeftestad L T, Gorzula S, Shrestha R K. 2021. Involuntary resettlement in Nepal: A portfolio review. Annual Conference (Virtual).

Yang H, Simmons B A, Ray R, et al. 2021. Risks to global biodiversity and indigenous lands from China's overseas development finance. Nature Ecology & Evolution, 5(11): 1520-1529.

Zhang X, Friedl M A, Schaaf C B, et al. 2003. Monitoring vegetation phenology using MODIS. Remote Sensing of Environment, 84(3): 471-475.


Zhao S, Wang Q, Li Y, et al. 2017. An overview of satellite remote sensing technology used in China's environmental protection. Earth Science Informatics, 10(2): 137-148.


Zhao S H, Wang Q, Zhang F, et al. 2014. Environmental applications of GF-1 satellite. Spacecraft Engineering, 23(S): 118-124.

Zheng Y, Wang S, Cao Y, et al. 2021. Assessing the ecological vulnerability of protected areas by using big earth data. International Journal of Digital Earth, 14(11): 1624-1637.