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Article

Study of Land Surface Changes in Highland Environments for the Sustainable Management of the Mountainous Region in Gilgit-Baltistan, Pakistan

by
Amjad Ali Khan
1,2,3,
Xian Xue
1,2,3,*,
Hassam Hussain
4,
Kiramat Hussain
5,
Ali Muhammad
3,6,
Muhammad Ahsan Mukhtar
3,6 and
Asim Qayyum Butt
3,6
1
Key Laboratory of Ecological Safety and Sustainable Development in Arid Lands, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China
2
Dry Lands Salinization Research Station, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China
3
University of Chinese Academy of Sciences, Beijing 100049, China
4
Department of Environmental Science, Karakorum International University Gilgit, Gilgit 15100, Pakistan
5
Forest, Wildlife & Environment Department Government of Gilgit-Baltistan, Gilgit 15100, Pakistan
6
Key Laboratory of Cryospheric Science and Frozen Soil Engineering, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China
*
Author to whom correspondence should be addressed.
Sustainability 2024, 16(23), 10311; https://doi.org/10.3390/su162310311 (registering DOI)
Submission received: 12 September 2024 / Revised: 14 November 2024 / Accepted: 21 November 2024 / Published: 25 November 2024
(This article belongs to the Special Issue Sustainable and Regenerative Ecologies for the Management of Mountain Forestscapes)
Browse Figures
Figure 1
<p>Study area map: (<b>a</b>) Pakistan’s map; (<b>b</b>) map of the Gilgit-Baltistan (GB) Province of Pakistan; (<b>c</b>) study area location, with a 10 km buffer along the CPEC route in three districts (Gilgit, Hunza, and Nagar) of Gilgit-Baltistan, Pakistan.</p> "> Figure 2
<p>Distribution of minimum, mean, and maximum NDVIs from 2008 to 2023.</p> "> Figure 3
<p>Spatial pattern of NDVI change due to build-up in the study area from 2008 to 2023.</p> "> Figure 3 Cont.
<p>Spatial pattern of NDVI change due to build-up in the study area from 2008 to 2023.</p> "> Figure 4
<p>Spatial pattern of NDVI change due to water in the study area from 2008 to 2023.</p> "> Figure 4 Cont.
<p>Spatial pattern of NDVI change due to water in the study area from 2008 to 2023.</p> "> Figure 5
<p>Distribution of NDWI from 2008 to 2023, with four-year intervals.</p> "> Figure 6
<p>Spatial change in NDWI from 2008 to 2023 and significant at 0.01, 0.05 level.</p> "> Figure 7
<p>Spatial change in NDBI from 2008 to 2023 and significant at 0.01, 0.05 level.</p> "> Figure 8
<p>Distribution of NDBI from 2008 to 2023, with four-year intervals.</p> "> Figure 9
<p>The trend of the aridity index in the study area.</p> "> Figure 10
<p>Shows (<b>a</b>) population dynamics and (<b>b</b>) tourist flow in the study area.</p> "> Figure 11
<p>Temporal variation and linear trend along the CPEC route from 2008 to 2023; annual precipitation in (<b>a</b>) Gilgit and (<b>b</b>) Hunza-Nagar; annual temperature in (<b>c</b>) Gilgit and (<b>d</b>) Hunza-Nagar.</p> "> Figure 11 Cont.
<p>Temporal variation and linear trend along the CPEC route from 2008 to 2023; annual precipitation in (<b>a</b>) Gilgit and (<b>b</b>) Hunza-Nagar; annual temperature in (<b>c</b>) Gilgit and (<b>d</b>) Hunza-Nagar.</p> ">
Versions Notes

Abstract

:
Highland ecologies are the most susceptible to climate change, often experiencing intensified impacts. Due to climate change and human activities, there were dramatic changes in the alpine domain of the China–Pakistan Economic Corridor (CPEC), which is a vital project of the Belt and Road Initiative (BRI). The CPEC is subjected to rapid infrastructure expansion, which may lead to potential land surface susceptibility. Hence, focusing on sustainable development goals, mainly SDG 9 (industry, innovation, and infrastructure) and SDG 13 (climate action), to evaluate the conservation and management practices for the sustainable and regenerative development of the mountainous region, this study aims to assess change detection and find climatic conditions using multispectral indices along the mountainous area of Gilgit and Hunza-Nagar, Pakistan. It has yielded practical and highly relevant implications. For sustainable and regenerative ecologies, this study utilized 30 × 30 m Landsat 5 (TM), Landsat 7 (ETM+), and Landsat-8/9 (OLI and TIRS), and meteorological data were employed to calculate the aridity index (AI). The results of the AI showed a non-significant decreasing trend (−0.0021/year, p > 0.05) in Gilgit and a significant decreasing trend (−0.0262/year, p < 0.05) in Hunza-Nagar. NDVI distribution shows a decreasing trend (−0.00469/year, p > 0.05), while NDWI has depicted a dynamic trend in water bodies. Similarly, NDBI demonstrated an increasing trend, with rates of 79.89%, 87.69%, and 83.85% from 2008 to 2023. The decreasing values of AI mean a drying trend and increasing drought risk, as the study area already has an arid and semi-arid climate. The combination of multispectral indices and the AI provides a comprehensive insight into how various factors affect the mountainous landscape and climatic conditions in the study area. This study has practical and highly relevant implications for policymakers and researchers interested in research related to land use and land cover change, environmental and infrastructure development in alpine regions.

1. Introduction

The sustainable development of mountain landscapes offers essential ecological services to human beings, and it is vital to recognize and enhance the regenerative ecology to minimize the dynamics that influence such mountain ecosystems [1]. Mountainous ecosystems have been assumed and influenced by climate change globally [2,3]. Mountains are vulnerable to rapid climate change [4], providing remarkable sites for the timely discovery and study of indications of climate change and its effects on the ecological system and society [5]. The world has faced substantial warming for the last few decades [6,7], causing profound influences on the physical and functional alteration of mountain ecology [8]. Changes due to global warming are causing ecological issues like water scarcity [9], dynamic changes in vegetation, land degradation, and disasters. These issues will significantly impact the ecosystem and the sustainable socio-economic development of the affected countries and regions [10,11]. Increased anthropogenic activities in these pristine highland environments can also trigger habitat disruption, further threatening these unique ecologies [12]. Therefore, the appraisal of ecosystem susceptibility performs a significant role in ecological management and sustainable development [13]. Extreme human activities and the rapidly changing climate have notably disturbed natural ecosystems [14], mainly alpine ecosystems, which extensively spread in high-altitude mountainous regions [15]. Several ecological development policies need to be implemented along the high altitude to encourage the sustainable development of the ecosystem and livelihood of communities in mountainous regions [16,17].
Various developmental schemes in economic corridors and their environmental effects have provoked immense global concentration and criticism [18,19]. The discussion also arose from the multifaceted relationship between human activities that degrade ecological health and the increasing susceptibility of Asia’s enormous environments [20,21]. For instance, these threats have increased due to substantial developmental projects under China’s Belt and Road Initiative (BRI). Notably, within the BRI, the massive developmental schemes designed and implemented under the China–Pakistan Economic Corridor (CPEC) are unfavorable for alpine regions, mainly for northern Pakistan. From this perspective, [22] recommended establishing an effective strategy for the BRI to instrument standards to reduce carbon emissions and encourage environmentally approachable development. The Chinese government started the BRI to encourage intercontinental integration, collaboration, peace, and economic growth. On the other hand, ecological crises and climate change signify possible threats to accomplishing these aims. Even though there are some concerns about environmental sustainability, these projects are also beneficial for the socio-economic development of the nations when the concept of the green economy (GE) is implemented. Theoretically, the idea of GE and green development (GD) is connected to the notion of sustainable development goals, particularly, SDG 9 (industry, innovation, and infrastructure), and stable socio-economic progress [23]. The Global Green Growth Institute states that GD is an innovative and novel development standard that maintains economic progress while promising environmental sustainability [24]. It should be highlighted that a GE aims to guarantee an increase in prosperity and enhance well-being and social uniformity while resisting the reduction of natural resources and decreasing environmental risks. The GE should be treated as an instrument whose goal is to realize sustainable development and as a component that integrates social, economic, and environmental goals, during the construction of CPEC projects [25]. The CPEC comprises several infrastructural projects containing a vast railway network, roads, fiber optics, and pipelines along the alpine region of northern Pakistan, and both nations promised to accomplish this mega project worth USD 62 billion by 2030 [26]. These projects are considered to exploit natural resources [27]. This region is home to the world’s largest glaciers and mountain ranges. This development project is an alarming threat to the local biodiversity, as well. The CPEC projects have reportedly initiated devastation to the alpine ecosystem at a considerable scale [28]. These schemes are clogging ecological concerns, with the possibility of causing environmental destruction beyond human thinking [29]. Similarly, CPEC projects are already altering atmospheric situations due to the increase in air temperature by triggering the melting of glaciers in the alpine region of Pakistan [30] and affecting agricultural land and water systems in high altitudes [31]. Furthermore, study [32] found in the literature states that there is an insignificant correlation among the variables found for environmental sustainability for road construction under CPEC projects, and the researchers suggest the environmental impact assessment index and implementation practices to reduce environmental destruction for long-standing sustainability. For the observation of such changes in the alpine ecosystem, remote sensing technology is extensively employed in various applications, such as vegetation change [33], urban expansion [34], and the extraction of water bodies [35]. Valid evidence regarding the spatial distribution of such land features is vital in numerous scientific fields [36]. For change detection studies, substantial information in multispectral images can be obtained using different image processing methods; among these, spectral index-oriented approaches are widespread, and generally [37], these methods give reliable outcomes [38]. Different band combinations of multispectral imageries have been developed to enhance delineation accuracy [39]. The normalized difference vegetation index (NDVI) is the commonly applied vegetation index [40]. It is considered an uncomplicated index because it assists in equalizing for varying illumination states, slopes, and other factors [41]. Another ratio, the normalized difference built-up index (NDBI), has been developed to analyze images to differentiate the built-up area [42]. Similarly, the normalized difference water index (NDWI) has been designed to delineate water bodies by combining bands to distinguish water pixels [38].
So far, numerous studies have been conducted on multispectral indices around the globe by utilizing multispectral data obtained through remote sensing (RS) to extract various land features, their mapping, and observing the changes with real-time data acquisition because of their cost-effectiveness and distinguished accuracy [43]. The NDVI has been applied by [44] in Bangladesh to quantify the change in forest cover from 1989 to 2010, revealing that 1.41% of forest decreased because of anthropogenic activities. In the Mediterranean, Turkey studied land transformation by employing NDVI. It observed that deforestation happened at low elevations, compared to small regions near roads, whereas evidence was found regarding forest restoration at high altitudes [45]. The study conducted in Pakistan by Liu et al. [46] used the NDBI and NDVI in their research to investigate the impact of vegetation cover and build-up on the urban heat island; they disclosed that vegetation cover decreases urban heat, whereas excessive build-up increases the effect of urban heat in particular places. Furthermore, a study was carried out in Hyderabad, India, using the NDVI, NDBI, and NDWI collectively to estimate urban surface patterns [47]. Likewise, a dynamic variation was found using the NDWI from 2001 to 2018 in the water extent area of Nainital Lake Uttarakhand, India. The results showed that the NDWI was considered a good technique, with 96.94% accuracy, and the trend in lake water spread area was determined based on calculated values of the NDWI [48]. The study presented by [49] employing NDWI multispectral characteristics focused on Qinghai Lake, China from 1986 to 2023; researchers indicated an increasing trend in the Qinghai Lake region, resulting in a turning point in 2004. Moreover, Shahdagh National Park in Azerbaijan was considered as a study area using the NDVI and NDWI multi-band methods, and based on the results, the vegetation cover decreased, which is more delicate due to anthropogenic reasons [50]. From 1985 to 2018 a study was conducted in Lake Dayet Awwa, Morocco. Landsat satellite images were utilized to calculate the NDWI, and it was observed that the surface area of the lake declined considerably between 1985 and 2018. This decrease can be justified by anthropogenic and natural factors [51]. Like the NDWI and NDVI, which are multispectral approaches, the NDBI is also a useful method in RS and GIS to detect surface changes. In Pachhua dun, Dehradun, Uttarakhand (India), researchers conducted a study from 1989 to 2020 utilizing the NDBI. The results found that the built-up areas have increased in size from 44.23 sq. km to 154.56 sq. km [52]. The study also investigated changes in LULC Kasur Pakistan from 1991 to 2021 by using the NDBI. The NDVI and NDBI values were recorded as +0.83 in 2021, whereas the lowest value observed was +0.65 in 1991, which had a strong negative correlation between the NDBI and NDVI [53]. Similarly, to monitor and predict the built-up areas in the Sleman Regency, Yogyakarta Special Region, Indonesia, a mapping was conducted by [54]. The results revealed that the built-up area spread by 12.84% from the years 2018 to 2022, and it is projected that in 2026, the area will have increased by 15.48%.
Limited regional-level studies have been conducted on the CPEC project, particularly by utilizing the NDVI, NDWI, and NDBI, which have still not been quantified in the alpine domain. No study using the abovementioned indices on the CPEC was found in the literature. Focusing on this research gap to measure the dynamics of land resources in the context of the alpine region of the CPEC is vital for progressing knowledge in the field of remote sensing and encouraging sustainable development goals, particularly SDG 9 (industry, innovation, and infrastructure) and SDG 13 (climate action), and ensuring that crucial economic corridors will be resilient to upcoming challenges. Concentrating on this research gap, we have utilized the multispectral indices approach, in addition to the aridity index (AI), to (1) study and detect variation in vegetation, water bodies, and build-up in the alpine domain of the CPEC and (2) evaluate the impacts of climate change (annual temperature, annual precipitation, and annual potential evapotranspiration) and anthropogenic activities (population and tourist flow) on the NDVI, NDWI, and NDBI in the alpine region of the CPEC.

2. Study Area and Data Collection

2.1. Study Area

Based on the current study area, as shown in Figure 1, the CPEC route has a 10 km buffer in three districts, Gilgit, Hunza, and Nagar, of Gilgit-Baltistan (GB) Province, Pakistan. The length of this route section is 274 km, and the area is 4901.79 km2 (calculated in ArcGIS10.8). The three districts are essential and located on the CPEC route. The Gilgit District is the provincial capital of GB. GB is a crucial region for the CPEC projects, as it is a gateway to China via the Karakoram Highway (KKH), which connects Pakistan to China’s Xinjiang Province. The geographical location of the Hunza District lies between 36°25′46″ N and 74°34′31″ E, Nagar is located between 36°16′54″ N and 74°43′51″ E, and the Gilgit District has geographical coordinates of 35.8819° N to 74.4643° E. Hunza and Nagar cover an area of 14,305 km2, and the Gilgit region covers an area of about 16,800 km2. A mean annual temperature of 2.8 to 6.5 °C was observed in the Hunza Valley at 2810 to 3669 m a.s.l. [55], and daily mean temperatures of −9.0 °C and 14.6 °C were observed in January and July, respectively [56]. The mean total annual precipitation in the Gilgit District is 132 mm, the elevation is 1460 m a.s.l [57], and the average temperatures range from about 3.6 °C in January to 26.9 °C in July. Similarly, the Nagar District has elevations ranging from 1800 to 3100 m a.s.l, with significantly colder temperatures and higher precipitation rates [58]. The climate of this region is changing, and its natural ecosystem is vulnerable to climate change, especially its effects, which include the melting of glaciers in the past and the degradation of landscapes. Mostly, people of this region are attached to agriculture and orchids for obtaining food, and cultivation is mainly focused on vegetables and other edible crops, like onions, carrots, peas, tomatoes, and cabbage [59], to maintain their economic conditions and living standards by selling agricultural products and fruits. The most popular fruit trees exist all over the region, such as apricots, cherries, mulberries, apples, grapes, and almonds [60]. Generally, the soil has low clay substances, silt, sandy fractions, and no organic matter. The soil is removed by the moderate water-holding capacity and usually looks shallow. The flora and fauna of the entire GB are unique due to its natural biophysical characteristics and ecological zones. Approximately 1000 species of vascular plants have been identified in the northern alpine area of Pakistan; because of its ecological zone, five critical types of forests exist in the region, which include sub-alpine, mountain dry temperate coniferous, mountain dry temperate broadleaved, mountain sub-tropical scrub, and northern dry scrub [61].

2.2. Data Collection

Landsat 30 m × 30 m spatial resolution-based satellite images were used, which cover the CPEC route in the study area. For the years 2008 and 2009, Landsat 5 (TM) was used, Landsat 7 (ETM+) was used for 2010, 2011, and 2012, Landsat 8 (OLI/TIRS) was used for 2013 to 2021, and Landsat-9 (OLI/TIRS) was used for 2022 and 2023. In total, 36 images were utilized, and these images were acquired from the United States Geological Survey (USGS) Earth Explorer website (https://earthexplorer.usgs.gov/) accessed on 20 June 2024. Different tiles of Landsat images were downloaded from 2008 to 2023 from this site. Additionally, to map the NDVI and NDWI, the summer season images have been used because, during this season, Landsat imageries are correct and precise for the NDVI and NDWI. Similarly, for the NDBI, the winter season images were used because the built-up area clearly shows the absence of tree leaves in the winter season; in the study area, due to the significant quantity of trees, it is impossible to identify the built-up area in the summer. The cloud covers < 5% of Landsat images chosen to collect data to confirm precision. When wholly covered clouds are unclear in the field, the data will not be accurate. Their specifications are shown in Table 1. The imagery dates were selected based on dry season, quality, and availability. Suitable band combinations have been used to measure indices. For the NDVI, the recommended bands are red and near-infrared; for the NDWI, the recommended bands are green and near-infrared; and similarly, to map the NDBI, the specified bands are shortwave infrared and near-infrared, all of which have been used in this study.
Moreover, the purpose of choosing the years 2008 to 2013 is that no such development was seen in this period, but after the CPEC initiative, dramatic changes were observed in the study area from the year 2013 onward. Therefore, to identify the dynamics of various features, the study period has been divided into two parts: the first part is before CPEC development, and the second is after CPEC development. Furthermore, the population and tourist data were also obtained from the Pakistan Statistical Department and Tourism Department Gilgit-Baltistan, and climate data were obtained from the Pakistan Meteorological Department.

3. Methodology

3.1. Pre-Processing of Data

The data have been customarily used to pre-process Landsat images for radiometric correction. Various steps have been implemented, such as band composition, mosaic to new raster, extraction by mask to pre-process satellite images, and correction of scanlines from Landsat 7 images. This process was performed using ArcGIS 10.8 and ENVI 5.6 software on images of the study region. Mosaicking of all required bands from 2008 to 2023 was performed using the “Mosaic to New Raster” tool of the Raster Data Set available in the “Data Management Tools: of ArcGIS 10.8 software. The “Raster calculator” tool of “Map Algebra” available in the Spatial Analyst extension of ArcGIS 10.8 was used to calculate the NDVI, NDWI, and NDBI. Finally, change maps have been developed for various years.

3.2. Normalized Difference Vegetation Index (NDVI)

First, the NDVI has been utilized to calculate the quantity of vegetation cover in a study area. Hence, it is highly beneficial to understand the variation in green vegetation areas. NDVI is one of the most widely used vegetation indexes by RED and near-infrared (NIR) bands of the electromagnetic spectrum and is utilized for the analysis of remote sensing images to obtain the vegetation information of the target area [62]. NDVI is a linear unification involving the near-infrared and red bands, which has been considered a key index for quantifying the green features of the Earth’s surface [63]. Various researchers utilize the NDVI approach for observing and mapping the vegetation cover using remote sensing techniques [64,65]. Through the NDVI, we assess plant coverage based on how plants absorb and reflect light at a particular frequency. The NDVI is an outstanding index of vegetation development situations and vegetation cover degree. If an area is covered by vegetation, then the NDVI value shows a positive number that enhances as the vegetation cover further improves [66].
The NDVI values, ranging from −1 to +1, are calculated based on near-infrared and red bands with high precision [67]. The negative values indicate non-vegetated areas, such as snow, rocks, surface water, and barren land, falling within the 0.1 to 0.2 range. The vegetation canopy, which is healthy and thick, falls in the 0.5 range, while thin vegetation cover falls in the 0.2 to 0.5 range. Moderated vegetation cover falls within the 0.4 to 0.6 range, and the highest probable vegetation cover includes values above 0.6 [68,69]. The NDVI has been calculated by applying Equation (1), as illustrated in [70], ensuring accurate and reliable results, as follows:
N D V I = N I R − R E D N I R + R E D
where NIR describes the near-infrared band, and RED indicates the red band.

3.3. Normalized Difference Water Index (NDWI)

Another ratio used in this study is the NDWI, which is extracted using Equation (2), as expressed in [70]. The NDWI recommended by [71] aims to increase water body reflectance in the green band and minimize water body reflectance in the NIR band [36,72]. The NDWI is estimated as follows:
N D W I = G r e e n − N I R G r e e n + N I R
The NDWI is applied in satellite images to differentiate open water characteristics by employing spectral bands of near-infrared (NIR) and visible green (GREEN) [73]. The NDWI has been considered a valuable index for distinguishing land and water because of the high-level absorption of electromagnetic radiation by the water’s surface. The NDWI values are in the −1 to +1 range, with 1 representing the existence of water bodies or very high humidity and −1 indicating dry regions or a moisture deficiency [71].

3.4. Normalized Difference Built-Up Index (NDBI)

The NDBI was suggested in 2003 as an emerging technique. The NDBI is used to map built-up areas. This approach was first used in China to obtain data for a built-up area of Nanjing City [42]. In this study, the NDBI was applied to extract the built-up area using Equation (3), as applied in [70]. The NDBI values range from −1 to + 1, and it has been suggested that positive values indicate the existence of built-up areas. In contrast, negative values describe the absence of the built-up regions and settlements [42]. In other words, the lesser values exhibit scattered build-up, and the greater values reveal densely built-up areas [74].
N D B I = S W I R − N I R S W I R + N I R
SWIR refers to the reflectance values in the shortwave infrared band, and NIR refers to the reflectance values in the near-infrared band. Values for SWIR and NIR are obtained from satellite images captured by sensors, such as Landsat, Sentinel, or similar platforms.

3.5. Aridity Index (AI)

The aridity index is a vital indicator of climatic severity, revealing the balance between annual precipitation and evapotranspiration (PET). It is a degree of dryness in a specific region. According to [75], AI values less than 0.03 show hyper-arid climates, values from 0.03 to 0.2 signify arid climates, values from 0.2 to 0.5 show semi-arid regions, values from 0.51 to 0.65 describe sub-humid areas, and values greater than 0.65 denote humid climatic regions. The monthly temperature, annual precipitation, and annual aridity index (AI) data from 2008 to 2023 have been evaluated for the study period. The study area consists of two meteorological stations. The Gilgit meteorological station has an elevation of 1460 m and a latitude of 350.55′ N. The Hunza meteorological station has an elevation of 2156 m, with a leeway of 360.19′ N, and no meteorological station exists in the Nagar District. Still, it is adjacent to the Hunza District, which has the same elevation and climate. The area is investigated using data on the annual precipitation, potential evapotranspiration (PET), and aridity index (AI) from two meteorological stations. Intending to examine the precipitation (P), temperature (T), and potential evapotranspiration (PET) trends of the research area, we have utilized all available meteorological data of two existing stations from 2008 to 2023. We have then estimated the PET by using the Thornthwaite method [76], according to the following equation:
P E T i = 16 d × [ ( 10 × T i I ) ] a
where Ti shows the mean monthly temperature, I represents an empirical factor estimated from the sum of the twelve mean air temperatures of each year in Equation (5), and d is a correction factor or adjustment factor that depends on the latitude and month of the met station. The notation “a” in Equation (4) represents a “constant” coefficient. While the coefficient value is dependent on the annual heat index, the coefficient is estimated using Equation (6) with the yearly heat index.
I = ∑ i = 1 12 ( T i 5 ) 1.514
a = 6.75 × 10 − 7 × I 3 − ( 7.71 × 10 − 5 ) × I 2 + ( 1.792 × 10 − 2 ) × I + 0.49239
The PET values are used to measure the aridity index, which is suggested by the United Nations Environment Program (UNEP, 1992) [77]. It is also used by many researchers [78,79,80]. They are calculated according to Equation (7), as follows:
A I i = P i P E T i
where Pi represents annual precipitation, and PETi represents potential evapotranspiration.
In Equation (7), precipitation (P) is a significant factor because the AI analyzes the balance between water gain (precipitation) and water loss (potential evapotranspiration). It helps to measure how dry or wet an area is. The formula appraises the availability of water (by precipitation) relative to the possibility of water loss. If Pi is much lower than PETi, the area is considered arid, which is vital for studying environmental factors. Without determining the precipitation, it cannot determine how much water is available to respond to the impacts of evapotranspiration. Therefore, its addition is theoretically sensible and authenticated by climatological and environmental studies.

3.6. Mann–Kendall (M-K) Test

The M-K test is a non-parametric test commonly employed to evaluate whether the trend linked with several factors is significant, and the t-test is applied to assess the results [81]; this method was developed by Mann and Kendall [82]. It is a statistical method to assess if there is a monotonic upward or downward trend of the variable of interest over time. A monotonic upward (downward) trend means that the variable consistently increases (decreases) over time, but the trend may or may not be linear. It does not need the variables to be independent and normally distributed. The corresponding formulas of the M-K test statistics are shown in Equations (8)–(10), as follows:
Z = s − 1 n ( n − 1 ) ( 2 n + 5 ) / 18 for S > 0 0 for S = 0 s + 1 n ( n − 1 ) ( 2 n + 5 ) / 18 for S < 0
S = ∑ k = 1 n − 1 ∑ j = k + 1 n S g n x j − x k
S g n x j − x k = + 1 if x j − x k > 0 0 if x j − x k = 0 − 1 if x j − x k < 0
Here, the Z value shows the following trend: when |Z| > Zα, the null hypothesis of the trend is accepted. In this study, α = 0.01 and α = 0.05 were used to define the significance levels, and |Z| > α/2 was equal to 2.58 and 1.96, respectively [83,84]. S represents the statistic of Kendall sum, and xj and xk are the parameter data values at times j and k, respectively.

4. Results

4.1. NDVI Distribution from 2008 to 2023 and Change Detection

A change detection using the NDVI was undertaken to evaluate the variation in vegetation in the study area. The detailed distribution results and values based on the high and low NDVI values from 2008 to 2023 are shown in Figure 2. The minimum (0.43) NDVI values occurred in 2008, and the maximum (1.0) NDVI values occurred in 2013 and 2018. The minimum and mean NDVI values show a significant increasing trend of 0.038/year at p < 0.05 and 0.017/year at p < 0.05. Similarly, an analysis of the NDVI values from 2008 to 2023 reveals a large fluctuation. Overall, the maximum NDVI decreased at a rate of −0.0469/year at p > 0.05 (R2 = 0.01292). In addition, the correlation between the minimum and mean NDVIs was found to be significant at p < 0.05. The minimum and mean NDVIs were relatively small in the years of 2009, 2012, 2016, and 2022. The range of high NDVI values between 0.43 and 1.0 indicates the presence of thin and thick vegetation cover along the CPEC route. These NDVI values suggest that the area has less healthy and less moderately healthy vegetation cover.
Furthermore, the NDVI values were calculated for each year, and then, change detection concerning build-up was identified for different years, such as 2008–2009, 2009–2010, 2010–2011, 2014–2015, 2015–2016, 2016–2017, 2020–2021, 2021–2022, and 2022–2023, as shown in Figure 3. In 2008, the vegetation was somewhat low, but the 2008 to 2013 trend shows good vegetation signs in the southern region. From 2008 to 2017, there was an abrupt decrease in vegetation in the Gilgit District region due to the impact of build-up and the huge transportation network due to CPEC activities. This part of the study area is the central hub of business and capital of Gilgit-Baltistan along the CPEC route. In 2018, we can observe some good signs of vegetation; this may be because the government of Pakistan started the Ten Billion Tree Tsunami Program in 2018 across the country to fight deforestation, restore damaged ecosystems, and alleviate the impacts of climate change [85]. However, not all plantations survived due to problems like harsh weather, lack of care, and unsuitable species. Overall, the area is facing rapid urbanization. The main triggering factor of the decrease in vegetation is urban growth; most people are converting their land to construct various types of infrastructure for rent purposes, including shops, guest houses, hotels, and restaurants, which affect vegetation cover. Furthermore, the conversion of the forest and trees into agricultural land is also one of the main factors of vegetation change in the area, but this trend is seen far less often and in areas located away from the CPEC route. Some parts of the north side of the study area, i.e., Hunza and Nagar, are also facing urbanization problems, and vegetation cover is gradually declining (Figure 3).
Our results are aligned with the research of [86], which states that anthropogenic activities are mostly responsible for initiating changes in vegetation cover. Furthermore, distinct vegetation types have dissimilar structures and water utilization approaches; consequently, the vegetation shows different responses to climate change and anthropogenic activities.
Similarly, the change detection in 2008–2009, 2009–2010, 2010–2011, 2014–2015, 2015–2016, 2016–2017, 2020–2021, 2021–2022, and 2022–2023 in Figure 4 illustrate the spatial patterns of NDVI change driven by water in the Gilgit, Hunza, and Nagar regions of Gilgit-Baltistan Province. These results reveal that over the years, there has been a fluctuating impact of water dynamics on vegetation cover, with certain areas showing the conversion of vegetation to water bodies, which is represented by the red color. From 2008 to 2011, red patches appeared on the central and north sides because, on 26 March 2009, a GLOF event occurred on the Ghulkin Glacier in Gojal Hunza [87], which affected the vegetation of the region on 4 January 2010; a landslide with significant mass occurred in the Hunza District that later hit a small town close to the Hunza-Nagar River called Sarat Village and formed a huge lake, killing 19 people and blocking the CPEC route for more than one month [88]. These red patches indicate limited areas where vegetation was overtaken by water, likely due to seasonal river expansion or increased water levels affecting lower vegetation zones. From 2014 to 2017, the red areas became more widespread, suggesting intensified water fluctuation, which was potentially caused by increased runoff or climatic factors, leading to more extensive vegetation inundation. This expansion of water bodies during this period might be attributed to seasonal changes, glacier melt, or heightened precipitation, particularly impacting the central regions of the study area.
The other five GLOF events in Hassanabad Village of the Hunza District that occurred in 2019 and 2022 led to the erosion of homes, farmland, and physical infrastructure located close to the Hassanabad Nullah (ravine) near the CPEC route. Figure 4 shows the results of a change detection carried out using the normalized difference vegetation index (NDVI). The index was utilized for this analysis as many of the regions surrounding the river are green vegetation and can be captured most appropriately through this index. As is clear from the figure below, vegetation losses took place, most notably along the river, until 2022 [89].
Across all maps, consistent blue areas highlight the stability of primary water bodies, while the central and southern parts, especially near Hunza and Nagar, exhibit more substantial transitions of vegetation to water compared to the northern Gilgit region until 2019; however, from 2022 to 2023, substantial changes in vegetation due to water can be observed in the northern region. The hydro-meteorological hazards frequently occur in these mountainous valleys, including landslides, rock falls, snow or ice avalanches, glacial lake outburst floods (GLOFs), and flooding [90]. This indicates that these areas or central regions are more vulnerable to seasonal water level variations, glacial melt, and related impacts. These findings underscore the influence of water fluctuations on vegetation distribution in this alpine region, with implications for water resource management and vegetation preservation that are essential for sustainable land use in the mountainous terrain along the CPEC route in this critical zone of Gilgit-Baltistan.

4.2. NDWI Change Detection from 2008 to 2023

Based on the analysis of NDWI for each year in the study area from 2008 to 2023, the results demonstrate a high NDWI range between 0.76 and 1.0 in the presence of water bodies and moisture throughout the study area. On the other hand, the low NDWI range between −0.38 and −1 shows the existence of no moisture and aqueous content. These results show a dynamic trend between the positive and negative values. According to the results shown in Figure 5, the positive value of NDWI for 2008 was 0.77. From 2013 to 2018, the high NDWI values were 0.76 to 1.0, demonstrating an increasing trend.
Furthermore, the overall change detection and significant test from 2008 to 2023, as shown in Figure 6, occurred along the CPEC route. The area shows a significant trend, accounting for 87.69% of total water bodies (Figure 6), of which 32.08% and 55.61% of the areas passed the significance test and demonstrated significant increases and slight increases at p < 0.01 and p < 0.05, respectively. Similarly, the area showing a decreasing trend has passed the considerable test of 12.3% of the total water body area. The most evident significant increasing trend has been found in mountainous regions of the study area, especially in the Hunza District. Similarly, the water bodies slightly decreased at p > 0.05, mainly in the most northern part of the study area. The fluctuation in water bodies along the CPEC route section in Gilgit, Hunza, and Nagar is due to climate change, anthropogenic activities, unsustainable construction, some meteorological factors, and uncontrolled urbanization. Our results are in line with [91], who suggested that the variation in water bodies could be categorized into different stages in terms of natural factors, human activities meteorological factors, and population growth. Among these, anthropogenic factors [92] were considered the main triggering force that instigated rapid dynamic changes in water bodies.

4.3. NDBI Change Detection from 2008 to 2023

The results revealed that the high NDBI ranges from 0.37 to 0.85 symbolize the presence of built-up areas along the CPEC route. Similarly, the negative NDBI ranges between −0.75 and −0.71 indicate the presence of classes other than build-up. Based on the results of the spatial distribution of the NDBI in Figure 7 along the CPEC route, a significant increasing trend at p < 0.01 with 83.58% and a prominent increasing trend can be found in the Gilgit region, which is the southern region of the study area. These findings highlight the need for further research to understand the implications of such rapid urbanization. A change variation has been undertaken using the NDBI to show the development of the built-up area in Gilgit, Hunza, and Nagar along the CPEC route from 2008 to 2023. The detailed high and low ranges obtained from the NDBI for each year have been summarized and represented in Figure 8.
From 2008 to 2018, the values showed an increasing trend in build-up, but after the period of 2018 to 2023, a decreasing change can be seen in the study area; this may be due to the outbreak of COVID-19 from 2020 to 2022 because people were stuck in their homes and never involved in any outside social and business activities. Similarly, slightly increasing urbanization can be seen at p < 0.05 with 16.41% in some areas of the study area along the CPEC route. The maximum change can be seen in the southern region of the Gilgit District because the capital of Gilgit-Baltistan has various facilities. Most people are moving toward this region, and some parts of the Hunza and Nagar Districts have faced urbanization issues along the CPEC route on the central and northern sides. In this study, the NDBI is a consistent index of urban development, which is consistent with the findings of previous studies [93].

4.4. Aridity Index Analysis

The station that has received the maximum annual precipitation from 2008 to 2023 is the Hunza-Nagar met station, compared to the Gilgit met station. This study illustrates higher PET values at the Gilgit station due to its low topography and high temperature. In contrast, Hunza and Nagar recorded low PET values due to low temperature and high elevation (Table 2). The AI results of this study uncovered that the areas are in arid and semi-arid conditions, which prevail at both the Gilgit and Hunza-Nagar meteorological stations. Both meteorological stations are in alpine regions, but due to their topography, the values of the AI demonstrate arid and semi-arid conditions from 2008 to 2023. The results of the AI in Table 2 show that the Gilgit climate is arid, whereas the environment of Hunza has been semi-arid throughout the years. The time series graph and linear trends of the annual aridity index at the Gilgit and Hunza-Nagar met stations are shown in Figure 9.
The AI results describe variation from region to region, as the Hunza-Nagar met station results show a significantly decreasing trend at p < 0.05 (−0.0262/year). At the same time, the Gilgit meteorological stations also detected decreasing and non-significant trends at p > 0.05 (−0.0021/year). The decreasing values of aridity mean the drying trend and the increased risk of drought, as the study area already has an arid and semi-arid climate. Less precipitation and increasing evapotranspiration are the key factors in changing the aridity index, which needs serious planning to minimize future drought hazards in the study area. It can also be observed from the results that the Gilgit District is facing severe water issues due to unsustainable urbanization, particularly after the reconstruction of the Karakoram Highway (KKH) from 1 August 2008, to 30 November 2013, which connects China and Pakistan; this route has been officially considered for CPEC activities since 2015.

5. Discussion

5.1. Impact of Human Activities on the NDVI, NDBI, and NDWI

Generally, anthropogenic actions and economic activities are considered responsible for changes in land surface features. Thus, the influence of human activities and population burden on natural resources, especially vegetation growth, must be considered. Satellite data and a literature review prove that increasing residential areas, unsustainable infrastructure, commercialization, and industrial development have influenced the globe’s water resources and vegetative covers. Similarly, human activities influenced vegetation cover and water bodies in Gilgit-Baltistan in terms of both restoration and destruction. According to Figure 2, decreasing trends have been seen in vegetation cover in the entire study period from 2008 to 2023. On the other hand, in 2018 and 2023, some improvement was observed in vegetation because most people planted fruit trees on their lands, especially with the high demand for cherry fruit in the study area. This indicates some excellent signs of vegetation cover in these periods. The authors of [94] suggested that instabilities caused by decreasing and increasing vegetation cover in some regions can happen due to population expansion caused by infrastructure development. Generally, such changes indicate that lands with the maximum vegetative levels, like grasslands, shrubs, and forests, were actively undergoing conversion to provide land for other competing land uses, such as grazing, agricultural, and settlement purposes [95].
Similarly, the fluctuation in water bodies (Figure 6) along the study route of the CPEC section in Gilgit and Hunza-Nagar is due to climate change, anthropogenic activities, unsustainable construction, and uncontrolled urbanization. Along this route, the Hunza River Basin is essential from the economic point of view of both China and Pakistan because the CPEC route passes through this basin. The basin is highly glaciated, and there is a high possibility of water hazards, mainly due to the massive risk of glacial lake outburst floods (GLOFs) [96]. In 2008, various GLOF events occurred, which disturbed the local communities’ livelihood, such as the GLOF events of 6th January 2008 in Passu, 2nd April 2008 in Ghulkin, 22–24 May 2008 in Ghulkin, and 14–15 June 2008 in Ghulkin [87]. In 2010, a vast landslide occurred, creating an artificial lake in the Hunza River, also known as Atta Abad Lake, which blocked the CPEC route for several months. The overall region of Hunza-Nagar is also considered prone to natural disasters due to its topography.
The CPEC has been considered an imminent economic promise between China and Pakistan. Through this corridor, the route between China and Pakistan has been improved, with various joint ventures established and projects being constructed. It acts as a doorway to share business and other industrial advantages between both countries. With the infrastructure improvement, especially roads, fiber optics, and other communication means, under the umbrella of the CPEC, the study area’s urbanization increased intensively, as shown in Figure 7. The better road network has also increased this region’s tourism industry; this is also one of the main reasons for the expansion of built-up areas because local communities constructed hotels, restaurants, and guest houses for their economic well-being along the alpine region of the CPEC [97]. Many domestic and international tourists moved toward this area, as shown in Figure 10b. Due to the lack of data, the tourist flow is mentioned after 2014, and the years from 2014 to 2022 are divided into three-year periods (2014 to 2016, 2017 to 2019, and 2020 to 2022) to check each three-year change. The highest tourist flow can be seen in the period of 2017 to 2019, which has caused urban development in the study area [98] for the following reasons, particularly in the Gilgit region: most people moved from different parts of Gilgit-Baltistan due to multiple pull factors, such as better education, industries, commerce, a sustainable living environment, and better infrastructure than rural areas. Due to such reasons, rapid urbanization is causing environmental and ecological problems relating to water issues, deforestation, land degradation, and pollution. While, on the one hand, it is thought that the CPEC will be a blessing for both nations, on the other hand, it is contributing possible danger to the local environment because of its extensive construction of roads and the vast amount of vehicle movement; this results in a threat to environmental sustainability and will lead to deforestation and flooding due to glacier melting and climate change [99].
Figure 10a describes the population growth from 2008 to 2023, and the study years are divided into four four-year periods (2008 to 2011, 2012 to 2015, 2016 to 2019, and 2020 to 2023) to understand the population growth in each four-year period. After 2008, the study area’s population continuously increased in three districts: Gilgit, Hunza, and Nagar. In particular, the Gilgit District faces a more significant population burden than the other two districts. Due to the increasing population and human activities, the climatic conditions of the study area fluctuate. High temperatures negatively impact vegetation because extreme warming may increase soil moisture evaporation and water shortages [100]. Extreme temperatures can cause glaciers at high altitudes to melt, ultimately creating dangerous conditions for local communities. After the development of the CPEC, various types of activities increased in the study area. In particular, transportation movement by local communities can be seen along the CPEC route for different purposes, such as business between Xinjiang, China, and Gilgit-Baltistan, Pakistan, which is because the government of China has provided visa-free facilities to residents of Gilgit-Baltistan. These residents can cross the Pakistan–China border and enter only the Xinjiang Province of China using a pass permit; due to this, maximum anthropogenic movement can also increase carbon emissions. These emissions will significantly diminish the glaciers’ mass and probably cause landslides, severe flooding, and GLOF events in these regions [101].

5.2. Impact of Climate Change on the NDVI, NDBI, and NDWI

Variations in precipitation and temperature have long been considered a critical sign of climate change. Numerous recent studies have described a noticeable warming trend with spatial irregularity in altitude regions compared to plain areas [102]. Similarly, earlier studies have proposed that the CPEC is highly vulnerable to climate change [103]. Meanwhile, most regions along the CPEC route fall into arid and semi-arid zones, where water resources still need to be improved [104]. Moreover, the outcomes of the aridity index (AI) of our study also suggest that the area is facing an arid and semi-arid climate, with a significantly decreasing trend at p < 0.05 (−0.0262/year) and non-significant trends at p > 0.05 (−0.0021/year), which is an alarming situation for locals.
The annual aridity index trend in the study area was investigated from 2008 to 2023. The trend analysis showed that the Gilgit meteorological stations have demonstrated a further decreasing aridity index trend. The significant decreasing trends of the aridity index were mainly observed in urban areas. Decreasing the quality and quantity of water resources also signifies a severely restricting factor for agriculture in the study area. These factors, comprising the rising aridity, can have unpleasant effects on agricultural production in the area. Hence, the outcomes of this study have significant importance for appraising water shortages and water reserves at the local and regional levels to forecast practical actions to handle aridity in susceptible areas.
Growing agricultural production and an expanding population have degraded the demand for water resources in this area [105]. The CPEC continuously raises possible pressures on the natural environment [106]. Infrastructural expansion is a primary threat to biodiversity and ecological sustainability. The recently emerged plans and projects of the CPEC cause climate change, which further generates an alarming condition for water resources, especially melting glaciers, and increased human settlement associated with the CPEC contributes to the pollution and degradation of water bodies [25].
According to Figure 11a,b, the temporal variation in precipitation along the CPEC regions indicates that the maximum annual rainfall was recorded in 2015 in Gilgit and 2008 in Hunza-Nagar. The minimum precipitation was identified in 2012 and 2019 in Gilgit and Hunza-Nagar, respectively. The annual precipitation data for the Gilgit and Hunza-Nagar Districts of Gilgit-Baltistan from 2008 to 2023 reveal significant fluctuations and a general declining trend over the observed period.
The results indicate that the annual precipitation decreased at a rate of −1.62 mm/year in Gilgit and a gentle negative rate of −17.99 mm/year at the Hunza-Nagar meteorological stations. Similarly, for the mean annual temperature from 2008–2023, for both Gilgit and Hunza-Nagar, the R values suggest coefficients of determination of 0.1505 and 0.3944, which implies that 15% and 39% of the association of mean annual temperature in the data is explained by the trend line. Furthermore, in Figure 11c,d, a positive temperature increase could be seen in Gilgit and Hunza-Nagar, with 0.023 mm/year and 0.118 mm/year, respectively. These outcomes are in line with the results of earlier studies [107,108,109], which found a positive trend in annual temperatures. However, a rapid upward trend was noticed in yearly temperatures during the study period. Therefore, this severe increase in annual temperature could be the effect of the hot period faced by the region along the CPEC route. These decreasing (increasing) trends in annual precipitation (temperature) highlight potential anxieties for the region’s water resources, agriculture productivity, and overall ecological balance. With the CPEC development, the environmental impacts, mainly reduced rainfall and rising temperature, could exacerbate water scarcity and affect agricultural productivity. Identifying these climatic changes is critical for progressing adaptive strategies and maintaining sustainable development along the CPEC route. Further, integrated planning and mitigation efforts are needed to develop regional resilience.

5.3. Suggestions

There is a vital appeal for supervising, formulating, and administrating against environmental risks and ecology-related problems by committed experts and scientists from both nations. The CPEC route should be built away from agricultural land, forest areas, and water resources. Vertical slopes, highly erodible soils visible to water and wind erosion, and deforestation should be reduced and prevented. The three main aspects, community contribution through information flow, accountability, and transparency, must be followed because environmental security and sustainable development goals cannot be achieved otherwise. Implementing a complete ecosystem observation program along the CPEC route, concentrating on regions with substantial land surface changes, is critical to understanding and managing the environmental impact. Establishing more protected areas and green practices in CPEC development will help preserve biodiversity and alleviate the unfavorable effects of CPEC development on natural habitats. Solid guidelines on land management must be imposed to stop deforestation, unsustainable construction, and land exploitation. It is mandatory to lessen the effects of urbanization on the land surface by endorsing the implementation of green infrastructure habits, like using porous pavements, green roofs, and sustainable drainage systems. The CPEC must provide economic opportunities without destroying the environment on which the local communities depend. The CPEC consists of various projects, so all stakeholders and individuals should support this mega project collectively at every level. Furthermore, water resource prevention approaches should be developed to tackle the dynamic variations examined in the NDWI by safeguarding unbiased access to water resources for local people and CPEC development projects along the route. Climate change adaptation and alleviation measures must be integrated into CPEC development schemes to tackle the noticeable changes in land surface and lessen the corridor’s carbon footprint. Promoting association among local communities, policymakers, and related stakeholders is crucial to guarantee participatory decision-making practices in CPEC development projects. Similarly, training and capacity-building procedures should be arranged for local communities to improve their knowledge about environmental protection, sustainable land use methods, and the possible effects of CPEC on their lives and incomes. Collaborations among government departments, academic institutions, civil society unions, and non-government organizations should be encouraged to share their understanding, capability, and best practices in sustainable urban growth. Data-driven visions must be utilized to update evidence-oriented decision-making and adaptive administrative plans, concentrating on opportunities and emerging challenges linked with urbanization along the CPEC route. Furthermore, to reduce carbon emissions, different electric vehicles should be adopted as an alternative to oil-oriented automobiles, and the resulting hazardous waste from these vehicles must be controlled and discarded carefully to sustain an environmentally friendly corridor [110]. Massive blasting must be discouraged for tunnel construction, and drilling techniques should be adopted. These recommendations encourage a balance between the CPEC’s economic benefits and the necessity for environmental sustainability and conservation in the Gilgit, Hunza, and Nagar regions. By incorporating these recommendations into the CPEC development processes, policymakers can work toward accomplishing a more stable and sustainable development path for the area.

5.4. Limitations of This Study

This study was performed in the alpine region of Gilgit-Baltistan, Pakistan, and only three districts were considered: Gilgit, Hunza, and Nagar. It is necessary to conduct future studies on other areas of China and Pakistan along the CPEC route to gain a better understanding. The CPEC is a dual-country mega-development project, so further reliable information and data can be collected and analyzed for future studies. This study used Landsat 30 m resolution data; higher-resolution data can be used in the future. Similarly, better and more robust statistical methods can be used in the future by researchers for the quantitative analysis of influencing factors along the CPEC route to detect changes in land cover.

6. Conclusions

In the current study, spectral indices, NDVI, NDWI, and NDBI, were utilized by employing Landsat 5 (TM), Landsat 7 (ETM+), and Landsat 8-9 (OLI-TIRS) data and ArcGIS 10.8, ENVI 5.6, and the aridity index (AI) to find the climatic conditions of the study area along the route of the CPEC in three districts, Gilgit, Hunza and Nagar, of Gilgit-Baltistan, Pakistan. In addition, dynamic changes in the water bodies and decreasing trends in vegetation cover were observed from 2008 to 2023. At the same time, an increasing trend was observed in built-up areas. From the study’s outcomes, it has been determined that the NDVI, NDWI, and NDBI can be applied as effective parameters for observing changes in vegetation cover, water bodies, and built-up areas. This change detection has suggested that the study area is facing rapid and severe construction along the alpine region of the CPEC in the form of hotels, guest houses, shops, and unsustainable infrastructural development, which illustrate pressure on other natural resources, leading to climatic changes in these alpine regions and a dynamic pattern of water bodies and vegetation cover. If they are ignored and left uncontrolled, these trends may augment severe eco-environmental dilemmas. Therefore, there is a dire need to consolidate national urban development policies and planning structures that achieve stability between economic viability and environmental sustainability.
Similarly, due to the variability in the aridity index, the region was found to be arid to semi-arid. The maximum rate of aridity was observed in Gilgit (−0.0021/year), which faces an arid climate. In contrast, the Hunza-Nagar met station showed a semi-arid climate, with an aridity rate of (−0.0262/year). Furthermore, it is the exclusive responsibility of the leaders and officials of both China and Pakistan to design policies and consider these suggested strategies to achieve environmental sustainability. This study has concluded that the three multispectral indices are fit to accurately describe the strength and extent of water bodies, vegetation, and built-up areas. Thus, they possess substantial prospects and possibilities in contemporary eco-environmental evaluations.
Additionally, a unification of these three spectral indices with the aridity index has been demonstrated to be an effective development when evaluating the distinctive approaches and existing ground-accurate data. Government officials and legislators have expected that they must persuade researchers to perform further investigations into such indices with other environmental and ecological parameters and deliver additional information about the existing dynamics. In particular, other aspects regarding the increasing population, topographies, and economic and social factors must be addressed in these studies. In addition to investigating and planning for the abovementioned matters, a community involvement approach is also mandatory to protect the eco-environmental standards of the alpine region of the CPEC at the local and national levels.

Author Contributions

The first author, A.A.K., performed all the research under the supervision of X.X., H.H., K.H. and A.M. contributed to the data analyses, and A.Q.B. and M.A.M. contributed to the discussion section. X.X. performed the identification of research ideas and methods, writing guidance and revision, and grant support. All authors have read and agreed to the published version of the manuscript.

Funding

The National Key Research and Development Program of China (2022YFF0801902) financially supported this research.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The data used to substantiate the findings of this research are accessible upon request from the corresponding author.

Acknowledgments

The authors would like to thank every person who collaborated on this research, mainly the Pakistan Meteorological Department, for providing climate data. Also, the authors would like to acknowledge the Tourism Department Gilgit and their support in providing tourist flow data. Completing this research could not have been possible without their help and support. The authors want to express their sincere gratitude to Abhishek Banerjee (NIEER) for his invaluable guidance throughout the development of this research. His expertise and support significantly contributed to the improvement of this work. The careful review and sincere suggestions by the anonymous reviewers and the editor helped further improve our manuscript, and they are gratefully acknowledged.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. Study area map: (a) Pakistan’s map; (b) map of the Gilgit-Baltistan (GB) Province of Pakistan; (c) study area location, with a 10 km buffer along the CPEC route in three districts (Gilgit, Hunza, and Nagar) of Gilgit-Baltistan, Pakistan.
Figure 1. Study area map: (a) Pakistan’s map; (b) map of the Gilgit-Baltistan (GB) Province of Pakistan; (c) study area location, with a 10 km buffer along the CPEC route in three districts (Gilgit, Hunza, and Nagar) of Gilgit-Baltistan, Pakistan.
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Figure 2. Distribution of minimum, mean, and maximum NDVIs from 2008 to 2023.
Figure 2. Distribution of minimum, mean, and maximum NDVIs from 2008 to 2023.
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Figure 3. Spatial pattern of NDVI change due to build-up in the study area from 2008 to 2023.
Figure 3. Spatial pattern of NDVI change due to build-up in the study area from 2008 to 2023.
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Figure 4. Spatial pattern of NDVI change due to water in the study area from 2008 to 2023.
Figure 4. Spatial pattern of NDVI change due to water in the study area from 2008 to 2023.
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Figure 5. Distribution of NDWI from 2008 to 2023, with four-year intervals.
Figure 5. Distribution of NDWI from 2008 to 2023, with four-year intervals.
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Figure 6. Spatial change in NDWI from 2008 to 2023 and significant at 0.01, 0.05 level.
Figure 6. Spatial change in NDWI from 2008 to 2023 and significant at 0.01, 0.05 level.
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Figure 7. Spatial change in NDBI from 2008 to 2023 and significant at 0.01, 0.05 level.
Figure 7. Spatial change in NDBI from 2008 to 2023 and significant at 0.01, 0.05 level.
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Figure 8. Distribution of NDBI from 2008 to 2023, with four-year intervals.
Figure 8. Distribution of NDBI from 2008 to 2023, with four-year intervals.
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Figure 9. The trend of the aridity index in the study area.
Figure 9. The trend of the aridity index in the study area.
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Figure 10. Shows (a) population dynamics and (b) tourist flow in the study area.
Figure 10. Shows (a) population dynamics and (b) tourist flow in the study area.
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Figure 11. Temporal variation and linear trend along the CPEC route from 2008 to 2023; annual precipitation in (a) Gilgit and (b) Hunza-Nagar; annual temperature in (c) Gilgit and (d) Hunza-Nagar.
Figure 11. Temporal variation and linear trend along the CPEC route from 2008 to 2023; annual precipitation in (a) Gilgit and (b) Hunza-Nagar; annual temperature in (c) Gilgit and (d) Hunza-Nagar.
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Table 1. Detailed collection of different Landsat data.
Table 1. Detailed collection of different Landsat data.
SatelliteAcquired DateSensorPath/RowResolutionBandsCloud CoverSource
Landsat 511 October 2008
3 November 2008
27 July 2009
27 August 2009
30 May 2009		TM149/34, 149/35
150/35
149/34
149/35
150/35		30 m1,2,3,4,5,7<5%USGS
Landsat 725 October 2010
16 October 2010
12 October 2011
3 October 2011
30 October 2012
1 July 2012		ETM+149/34
150/35
149/34
150/35
149/35
150/35		30 m1,2,3,4,5,7<5%USGS
Landsat 89 October 2013
28 July 2013
26 October 2014
3 October 2014
31 October 2015
19 August 2015
17 October 2016
20 July 2016
1 August 2017
9 September 2017
4 August 2018
30 April 2018
15 January 2018
24 September 2019
13 July 2019
25 August 2020
16 August 2020
24 July 2020
4 September 2021
29 September 2021
15 October 2021		OLI/TIRS149/34, 149/35
150/35
149/34, 149/35
150/35
149/34, 149/35
150/35
149/34, 149/35
150/35
149/34, 149/35
150/35
149/35
149/34
150/35
149/34, 149/35
150/35
149/34
150/35
149/35
150/35
149/34
149/35		30 m1,2,3,4,5,6,7,9<5%USGS
Landsat 99 October 2022
8 September 2022
9 August 2023
10 August 2023		OLI/TIRS150/35
149/34, 149/35
150/35
149/34, 149/35		30 m1,2,3,4,5,6,7,9<5%USGS
Table 2. Climatic characteristics in Gilgit and Hunza-Nagar meteorological stations.
Table 2. Climatic characteristics in Gilgit and Hunza-Nagar meteorological stations.
YearsGilgit (Met Station)Hunza (Met Station)
PETAnnual PAIPETAnnual PAI
2008859.039227.70.2651695.120883.31.270
2009875.114150.10.1715692.513434.30.627
2010864.919155.30.1796633.270158.80.251
2011899.940132.60.1473638.878238.80.374
2012900.55185.90.0954669.753202.90.301
2013937.374187.30.1998650.041153.30.236
2014867.539145.60.1678694.539138.10.199
2015847.236270.80.3196676.368180.30.267
2016916.480158.70.1732692.552167.40.242
2017868.830151.20.1740721.020163.80.227
2018931.879154.50.1658711.559122.40.172
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2020876.597153.40.1750670.712492.10.734
2021891.157169.50.1902650.596198.30.305
2022885.896128.20.1447680.725190.00.279
2023898.065150.70.1678678.664147.80.218
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MDPI and ACS Style

Khan, A.A.; Xue, X.; Hussain, H.; Hussain, K.; Muhammad, A.; Mukhtar, M.A.; Butt, A.Q. Study of Land Surface Changes in Highland Environments for the Sustainable Management of the Mountainous Region in Gilgit-Baltistan, Pakistan. Sustainability 2024, 16, 10311. https://doi.org/10.3390/su162310311

AMA Style

Khan AA, Xue X, Hussain H, Hussain K, Muhammad A, Mukhtar MA, Butt AQ. Study of Land Surface Changes in Highland Environments for the Sustainable Management of the Mountainous Region in Gilgit-Baltistan, Pakistan. Sustainability. 2024; 16(23):10311. https://doi.org/10.3390/su162310311

Chicago/Turabian Style

Khan, Amjad Ali, Xian Xue, Hassam Hussain, Kiramat Hussain, Ali Muhammad, Muhammad Ahsan Mukhtar, and Asim Qayyum Butt. 2024. "Study of Land Surface Changes in Highland Environments for the Sustainable Management of the Mountainous Region in Gilgit-Baltistan, Pakistan" Sustainability 16, no. 23: 10311. https://doi.org/10.3390/su162310311

APA Style

Khan, A. A., Xue, X., Hussain, H., Hussain, K., Muhammad, A., Mukhtar, M. A., & Butt, A. Q. (2024). Study of Land Surface Changes in Highland Environments for the Sustainable Management of the Mountainous Region in Gilgit-Baltistan, Pakistan. Sustainability, 16(23), 10311. https://doi.org/10.3390/su162310311

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