Assessing the Land Surface Temperature Trend of Lake Drūkšiai’s Coastline

Abstract

This study investigates long-term land surface temperature (LST) trends along the shoreline of Lake Drūkšiai, a transboundary lake in eastern Lithuania that formerly served as a cooling reservoir for the Ignalina Nuclear Power Plant (INPP). Although the INPP was decommissioned in 2009, its legacy continues to influence the lake’s thermal regime. Using Landsat 8 thermal infrared imagery and NDVI-based methods, we analysed spatial and temporal LST variations from 2013 to 2024. The results indicate persistent temperature anomalies and elevated LST values, particularly in zones previously affected by thermal discharges. The years 2020 and 2024 exhibited the highest average LST values; some years (e.g., 2018) showed lower readings due to localised environmental factors such as river inflow and seasonal variability. Despite a slight stabilisation observed in 2024, temperatures remain higher than those recorded in 2013, suggesting that pre-industrial thermal conditions have not yet been restored. These findings underscore the long-term environmental impacts of industrial activity and highlight the importance of satellite-based monitoring for the sustainable management of land, water resources, and coastal zones.

1. Introduction

Global warming, ecosystem destruction, and landscape degradation have a profound impact on water bodies, contributing to swamping and eutrophication [1]. These processes disrupt the water cycle and pose serious threats to inland aquatic ecosystems. Human activities near water bodies, such as industrial contamination and the discharge of insufficiently treated wastewater, exacerbate water pollution and degrade water quality. Temperature imbalances accelerate evaporation rates, resulting in intense precipitation events such as heavy rain and snowstorms [2]. These phenomena can lead to flooding and further deterioration of water quality. Elevated temperatures may lead to thermal stratification in lakes, reduce oxygen levels, and adversely affect aquatic life [3]. Changes in water levels, driven by both rising temperatures and anthropogenic pressures, have increased nutrient inflow into lakes, triggering ecological shifts such as eutrophication, disruptions in food chain structures, and seasonal hypoxia of bottom sediments. These transformations are preserved as long-term records in sediment layers [1].

Ongoing changes in water level also influence the growth and species composition of coastal and aquatic vegetation, often leading to siltation. While waterlogging can promote the formation of wetlands, critical ecosystems for biodiversity, excessive siltation can disrupt existing habitats, alter hydrological flows, and contribute to the accumulation of organic matter, thereby degrading water quality [4]. An overabundance of organic compounds further intensifies eutrophication. Eutrophication refers to the process by which water bodies become excessively enriched with nutrients, particularly nitrogen and phosphorus [5]. This phenomenon is especially common in small, stagnant water bodies such as lakes, fishponds, and reservoirs [6]. The resulting nutrient surplus promotes the overgrowth of algae and aquatic plants, leading to harmful algal blooms, hypoxia (low oxygen levels), and the creation of “dead zones” where aquatic life cannot survive. The primary causes of eutrophication are agricultural runoff, wastewater discharges, and industrial pollution.

Identifying vulnerable areas based on water temperature variations is essential for early detection of ecological changes and for implementing effective management strategies. A variety of methods are available for monitoring water bodies and coastlines, among which remote sensing techniques are particularly notable for their high efficiency, wide spatial coverage, and ability to deliver continuous observations at relatively low cost [7,8]. Satellite imagery provides a comprehensive spatial overview of surface temperature (ST) changes over time. In addition, hydrodynamic modelling enables the simulation of thermal dynamics in lakes under various scenarios, offering insights into potential long-term impacts on lake thermal regimes. Aerial surveys complement these methods by delivering high-resolution imagery that captures detailed information about water bodies and coastlines, facilitating precise change detection over time.

In the context of land change analysis, thermal monitoring of water bodies using satellite data provides critical insights into how anthropogenic pressures and climate variability reshape land–water interfaces. Remote sensing-based LST analysis is increasingly acknowledged as a powerful geospatial tool for detecting land degradation, wetland transformation, and post-industrial landscape recovery. This approach supports sustainable land management objectives by enabling spatially explicit assessments of environmental change.

Numerous algorithms and methods are available to estimate LST using remote sensing data [9]. A variety of plugins and software tools have been developed for platforms such as QGIS (the Quantum Geographic Information System), ERDAS (the Earth Resource Development Assessment System), the ESA (Europe Space Agency) platform, SNAP (Sentinel’s Application Platform), and online environments like Google Earth Engine. However, these methods often require extensive software configuration, satellite data preprocessing, and a high level of technical expertise.

To address these challenges, the Remote Sensing Laboratory (RSLab) has developed a dedicated system for retrieving LST using thermal infrared sensor data from Landsat 5, 7, and 8 in combination with surface emissivity data from instruments such as the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and the Moderate Resolution Imaging Spectroradiometer (MODIS), employing Normalised Difference Vegetation Index (NDVI)-based methods [10,11]. This study employs Landsat 8 imagery, which does not inherently include ASTER and MODIS radiance data, as these originate from separate satellite systems. However, ASTER and MODIS radiance products can be integrated using the Split Window Algorithm [12] to enhance the accuracy of LST retrievals from Landsat 8 thermal infrared data. Landsat satellites provide multispectral imagery, including visible and near-infrared (NIR) bands, which are used to calculate NDVI. NDVI-based radiation models are particularly effective in areas with heterogeneous land cover. The Single Channel (SC) algorithm developed by Jiménez-Muñoz et al. [10,13] is employed in this study for LST estimation.

Sustainable environmental monitoring practices are essential for maintaining ecological balance and ensuring the resilience of water bodies amid ongoing environmental changes. By integrating advanced remote sensing technologies with in situ verification, we can effectively monitor and manage the thermal dynamics of aquatic systems, thereby promoting sustainability and safeguarding ecosystems from the long-term consequences of industrial activities. In this context, Lake Drūkšiai serves as a compelling case study for examining long-term thermal dynamics in a post-industrial aquatic environment. As Lithuania’s largest lake and former cooling reservoir for the INPP, it has undergone significant anthropogenic thermal stress. The decommissioning of the INPP in 2009 marked a pivotal moment, creating an opportunity to evaluate the lake’s thermal recovery and identify any persistent or emerging anomalies. This makes Lake Drūkšiai an ideal subject for assessing the effectiveness of satellite-based LST monitoring in capturing the legacy impacts of industrial activity on land–water systems. Only a limited number of studies have investigated the thermal effect of the INPP during its operational period, most of which relied on short-term or point-based in situ measurements. These methods, while valuable, have limited spatial coverage and do not capture the broader thermal dynamics of the lake after the plant was decommissioned in 2009. Furthermore, few studies have applied satellite-based LST retrieval methods to assess long-term post-industrial impacts on water systems. This study addresses this gap by applying NDVI-based LST analysis using Landsat 8 imagery to deliver a spatially detailed and temporally consistent assessment of Lake Drukšiai’s thermal regime over more than a decade.

The primary aim of this research is to investigate the thermal regime of Lake Drūkšiai and assess the long-term and ongoing impacts of the INPP, which was decommissioned in 2009. Although the INPP is no longer operational, its historical influence on the lake’s thermal dynamics remains significant. Lakes also play crucial roles in hydrological cycles and regional climate regulation [14]. This study introduces a novel approach by applying satellite-based remote sensing (Landsat imagery, available since 2013) to monitor LST across the entire lake, including areas beyond the traditional in situ monitoring points (E1–E6).

The goal of this research is not only to observe long-term temperature trends but also to identify spatial anomalies that may indicate emerging environmental risks, such as illegal wastewater discharges from local residents or industries. By expanding the spatial scope of monitoring and integrating satellite-derived data, this study contributes to the advancement of sustainable and proactive environmental surveillance strategies for aquatic ecosystems. These findings offer new insights, demonstrating that even 15 years after the closure of the INPP, the lake’s thermal regime has not reverted to pre-industrial levels. This persistent thermal imbalance reflects the prolonged and substantial impact of industrial activities on the lake’s ecosystem. In contrast to earlier studies that primarily relied on short-term or localised measurements, this research employs long-term satellite-based LST analysis to uncover spatial anomalies and sustained thermal effects across the entire lake. Furthermore, this study demonstrates how geospatial techniques can be effectively applied to post-industrial land change analysis, providing a replicable framework for other regions undergoing ecological recovery.

2. Materials and Methods

2.1. Description of the Study Area

The INPP was a large nuclear facility located near the city of Visaginas, Lithuania. It was one of the largest nuclear power plants in Europe, equipped with two Reaktornaya Bolshoy Moshchnosty Kanalnogo (RBMK-1500 designed and built by the Soviet Union) reactors [15]. Preparations for the construction of the INPP began in 1974. Reactor 1 was commissioned on 31 December 1983, and Reactor 2 commenced operation on 31 August 1987 [16]. Following the Chernobyl accident in 1986, the INPP underwent multiple international studies and extensive safety analyses. Although it was determined that the probability of an accident and the overall safety levels were comparable to Western nuclear standards, RBMK-type reactors, unlike modern nuclear reactors, did not include a protective shield capable of containing radioactive material during an accident. Consequently, Western policymakers and organisations agreed that the operational risks of RBMK reactors could not be sufficiently reduced to allow their permanent safe operation [16]. Following government resolutions, Unit 1 of the INPP was shut down at the end of December 2004, and Unit 2 was closed on 31 December 2009. During its 26 years of operation, the INPP produced approximately 307.1 billion kWh of electricity: 136.9 billion kWh from Unit 1 and 170.2 billion kWh from Unit 2. The total electricity sold amounted to 279.8 billion kWh. The closure of the INPP had a significant impact on Lithuania’s energy sector and economy, necessitating the diversification of energy sources and significant investment in renewable energy. The closure also resulted in job losses and economic hardship in the Ignalina region [15]. Lake Drūkšiai served as a cooling reservoir for the INPP for many years. It is the largest lake in Lithuania, located approximately 2 km south of the Latvian border, within the Ignalina and Zarasai districts, and extending into Belarus (coordinates: 55°37′20.5″ N 26°34′55.6″ E). The lake spans an area of 44.87 km², of which 8.58 km² lies within Belarus. Its shoreline extends for 74.49 km, and the average depth is 7.6 m, with a surface elevation of approximately 141.6 m (Figure 1). This transboundary water body represents a critical land–water interface, where industrial legacy, ecological processes, and geopolitical boundaries converge. The prolonged use of Lake Drūkšiai for thermal discharge has significantly altered its shoreline dynamics, sedimentation patterns, and thermal regime, making it an important subject for land change analysis and post-industrial landscape monitoring.

On the southern shore of the lake lies the village of Drūkšiai, where a hydroelectric power plant was built in 1953. During the construction, the source of the Prorva River was flooded, which resulted in a rise in Lake Drūkšiai’s water level by approximately 30 cm. The INPP operated south of Drūkšiai from 1983 until 2009, using lake water as a coolant for its reactors [17].

Anthropogenic climate change imposes considerable thermal stress on water bodies; in the case of Lake Drūkšiai, the lake was directly affected by the discharge of cooling water from the INPP [18]. Investigations have shown that the average surface temperature of Lake Drūkšiai increased by approximately 2.7 °C between 1984 and 1998 [19]. During this period, more than 10% of the lake’s surface often remained unfrozen in winter. The closure of the INPP in 2009 likely introduced new ecological conditions to Lake Drūkšiai, particularly due to alterations in its thermal regime [17]. Research indicates that even today, small amounts of chemical and radioactive pollution resulting from the INPP’s operation remain in the lake sediments. These pollutants are further augmented by domestic wastewater discharged from the sewage systems of the city of Visaginas and the INPP [20]. Therefore, it is crucial to continue monitoring the thermal regime of Lake Drūkšiai. The lake’s thermal regime, referring to its temperature dynamics and seasonal patterns, was significantly altered due to discharges from the plant. Following the closure of the INPP, a substantial shift in the lake’s thermal characteristics was anticipated. Hydrologists from the Lithuanian Energy Institute had already been monitoring the thermal state of the lake during the INPP’s operational period between 1981 and 1998 [19].

In this research, we used multispectral Landsat 8 satellite data with 30 m spatial resolution from the thermal infrared sensor (TIRS). The analysed images were captured between 2013 and 2024. The data are freely available through the U.S. Geological Survey (USGS) database [21]. For Landsat 8 Operational Land Imager (OLI)/TIRS products, it typically takes approximately 14 to 16 days to process scenes to a Tier 1 or Tier 2 product level, during which refined TIRS instrument line-of-sight model parameters are applied to real-time scenes.

This study employed Level-2 Science Products (L2SPs), which include surface reflectance (SR) and surface temperature (ST) data, intermediate bands used for ST product calculations, and quality assessment (QA) masks that indicate the usability of pixel data.

SR measures the fraction of incoming solar radiation reflected by the Earth’s surface toward the Landsat sensor. The Landsat Ecosystem Disturbance Adaptive Processing System (LEDAPS) and Land Surface Reflectance Code (LaSRC) algorithms correct for temporally, spatially, and spectrally varying scattering and absorption effects caused by atmospheric gases, aerosols, and water vapour. These corrections are necessary to characterise the Earth’s land surface reliably [21]. ST is measured in Kelvin units and is an important geophysical parameter in global energy balance studies and hydrological modelling. ST data are also useful for monitoring crop and vegetation health, assessing extreme heat events (such as volcanic eruptions and wildfires), and analysing urban heat island effects. Landsat 8’s OLI sensor generates nine spectral bands, covering the coastal, blue, green, red, NIR, shortwave infrared 1 (SWIR-1), shortwave infrared 2 (SWIR-2), and cirrus bands, all with 30-metre spatial resolution. A combination of data from Landsat 8 OLI RGB (Red, Green, and Blue) bands is visualised in Figure 2.

Landsat 8’s TIRS captures two thermal bands—TIR-1 (Band 10) and TIR-2 (Band 11), which measure the Earth’s emitted thermal energy. Both bands operate in the long-wavelength infrared range and have a spatial resolution of 100 m.

Landsat 8 imagery has been available from the USGS since 2013 and continues to be accessible to the present day.

2.2. Processing of Landsat 8 Data

The processing of Landsat 8 imagery for estimating LST based on the NDVI involves several standard steps. These methods are widely recognised and have been employed in various studies without modification [22]. The procedure is illustrated in Figure 3.

Band 4 (red) and Band 5 (NIR) were used to compute NDVI. Band 10 and Band 11, located in the thermal infrared (TIR) region, are capable of detecting thermal radiation emitted from the Earth’s surface. Unlike weather stations that measure air temperature, these bands record the land surface temperature, which is often significantly higher than the surrounding air temperature. The NDVI-based method was selected due to its ability to dynamically estimate surface emissivity based on vegetation cover, which is crucial for accurate LST retrieval in spatially heterogeneous environments. This method is particularly well-suited for areas with mixed land cover, such as lake shorelines, and is fully compatible with the SC algorithm employed in this study.

The processing stages for Landsat 8 data are summarised as follows:

• First, the NDVI is calculated using the red (Band 4) and NIR (Band 5) bands from the Landsat 8 imagery. NDVI values are essential for estimating surface emissivity, which is a key parameter for accurate LST retrieval. The relationship between NDVI and emissivity is based on the assumption that vegetation cover significantly influences the thermal emission properties of the surface.
• Next, surface emissivity is estimated based on the NDVI values. Pixels with NDVI values less than 0.2 are classified as bare soil, while those with values greater than 0.5 are considered fully vegetated. For mixed pixels (NDVI between 0.2 and 0.5), emissivity is calculated as a weighted average, reflecting the proportion of vegetation cover [23,24].
• Finally, LST is calculated using the previously derived surface emissivity and brightness temperature values. For this purpose, the SC algorithm, developed by Jiménez-Muñoz et al. (2009), is employed [13]. This algorithm utilises thermal infrared data acquired by satellite and applies atmospheric corrections to account for the influence of atmospheric conditions on the observed temperature.
• The processing workflow ensures that surface emissivity is accurately estimated across diverse land cover types, which is essential for enhancing the precision of LST retrievals from satellite observations. All methods applied in this study are well-established and have been appropriately cited to acknowledge the contributions of the original authors [10,13,22,24,25].
• Due to the limited availability of cloud-free Landsat 8 imagery over Lake Drūkšiai, only six representative acquisition dates between 2013 and 2024 were selected for analysis. These dates were chosen based on low cloud cover, seasonal consistency, and alignment with in situ measurements conducted by INPP environmental specialists, enabling partial validation of satellite-derived LST values.

The complete workflow for data processing, computation, and analysis is illustrated in Figure 4.

Figure 4. Workflow of data, calculation, and analysis of this research (Source: authors). For detailed numerical values and clearer visual representation of NDVI and LST results, please refer to Figure 5 and Figure 6, where the data are presented with appropriate scaling and legible legends.

Figure 4. Workflow of data, calculation, and analysis of this research (Source: authors). For detailed numerical values and clearer visual representation of NDVI and LST results, please refer to Figure 5 and Figure 6, where the data are presented with appropriate scaling and legible legends.

Figure 5. NDVI values on 18 June 2020 (Source: authors).

Figure 5. NDVI values on 18 June 2020 (Source: authors).

Figure 6. Survey points (red dots) LST results from 22 June 2013, 5 June 2015, 3 May 2018, 18 June 2020, 11 June 2023, and 16 August 2024 dates (Source: authors).

Figure 6. Survey points (red dots) LST results from 22 June 2013, 5 June 2015, 3 May 2018, 18 June 2020, 11 June 2023, and 16 August 2024 dates (Source: authors).

Vegetation density is assessed by analysing the reflectance values from Band 4 (red) and Band 5 (NIR). Healthy green vegetation reflects a higher amount of energy in the NIR spectrum and absorbs more in the red spectrum due to photosynthesis. In contrast, stressed, diseased, or dead vegetation shows reduced reflectance in the NIR range. Non-vegetated surfaces such as clouds, water bodies, and snow typically reflect more energy in the visible spectrum than in the NIR range, while the reflectance contrast is minimal for surfaces like rocks and bare soil. NDVI values close to zero generally indicate bare soil or rock surfaces, while negative NDVI values correspond to water, snow, or cloud cover. The vegetation signal can be enhanced relative to the background by computing the ratio or difference between the red and NIR reflectance values.

NDVI is calculated using the reflectance values from Band 4 (red) and Band 5 (NIR). The raw Landsat 8 data, expressed as digital numbers (DNs), are first converted to top-of-atmosphere (TOA) spectral radiance (measured in W/m²/sr/μm). Subsequently, SR, brightness temperature (BT), and NDVI values are calculated using standard equations as referenced in [24,25,26]. The proportion of vegetation (PV) is determined by NDVI values using the following formula:

$$PV={\left[{\displaystyle \frac{NDVI-NDV{I}_{min}}{NDV{I}_{max}+NDV{I}_{min}}}\right]}^2$$

(1)

where NDVI_min and NDVI_max represent the minimum and maximum NDVI values observed in the dataset.

The estimated PV values are then used to calculate LSE. Satellites equipped with thermal infrared sensors such as MODIS, ASTER, and Landsat measure radiance, which is thermal energy emitted by the Earth’s surface. ST can be estimated from this radiance using the known physical properties of the sensor and Planck’s law, which describes the relationship between radiance and the temperature of an emitting body. After estimating surface temperature, emissivity is derived by comparing the measured radiance to that of a perfect blackbody at the same temperature. Accurate emissivity estimation is essential for reliable LST retrieval from satellite data.

Algorithms are employed to account for various factors that influence surface emissivity, including surface type, vegetation cover (as indicated by NDVI), soil moisture, and surface roughness. Typically, three types of emissivity estimation approaches are considered: NDVI-based emissivity, ASTER-derived emissivity, and MODIS-derived emissivity [27]. The relationship between NDVI and surface emissivity is grounded in the assumption that vegetation cover significantly affects thermal emissivity properties [25,28]. To differentiate between various surface types, researchers apply the NDVI threshold method [23,24,25,28], classifying pixels as follows:

• NDVI < 0.2—pixels are considered bare soil, and emissivity is derived from soil reflectance values.
• NDVI > 0.5—pixels are considered fully vegetated, and a typical constant emissivity value is assumed (approximately 0.99).
• 0.2 ≤ NDVI ≤ 0.5—pixels are classified as mixed (soil and vegetation), and emissivity is calculated as a weighted average based on the proportion of vegetation.

This approach ensures that surface emissivity is accurately estimated across diverse land cover types, which is essential for enhancing the precision of LST retrievals from satellite observations [28].

In general, NDVI values range from −1.0 to 1.0. Negative values typically indicate the presence of water, snow, or clouds, while values close to zero correspond to bare soil or non-vegetated surfaces. Moderately positive NDVI values (0.1–0.5) are associated with sparse or patchy vegetation, whereas values above 0.6 indicate dense and healthy vegetation cover.

The simplified formula for estimating emissivity (ε) is given as

$$\epsilon =\epsilon vPV+\epsilon s\left(1-PV\right)+de,$$

(2)

where εv and εs denote the emissivity values for vegetation and soil, respectively, and PV is the proportion of vegetation cover.

For flat surfaces, the cavity effect correction term (de) is typically negligible. However, for rougher surfaces, such as forested areas, this term can contribute up to 2% to total emissivity [29].

In this study, NDVI thresholds of 0.2 and 0.5 were applied to classify different surface types. To maintain consistency, NDVI values were standardised: all pixels with NDVI < 0.2 were assigned a value of 0.2, and those with NDVI > 0.5 were set to 0.5.

Since April 2016, an alternative approach for estimating soil surface emissivity has involved using the average emissivity value derived from the ASTER spectral library [30], filtered according to the TM6 band response function. Based on an analysis of 49 soil spectra, a mean emissivity value of 0.973 was obtained, with a standard deviation of 0.004.

Using these reference values (TM6 soil emissivity set at 0.97 and vegetation emissivity at 0.99), the final expression for LSE is given as

$${\epsilon }_{TM6}=0.004\mathrm{PV}+0.986.$$

(3)

Formulas (2) and (3) are sequentially applied in the process of LST retrieval. First, the PV, derived from NDVI values, is used in Formula (2) to estimate surface emissivity by accounting for the relative contributions of vegetated and bare soil areas. This emissivity value is then inserted into Formula (3) to adjust the brightness temperature, enabling accurate calculation of the LST. This stepwise approach ensures that surface heterogeneity is properly addressed, which is critical for improving the accuracy of thermal measurements derived from satellite data.

MODIS is an instrument aboard NASA’s Terra and Aqua satellites. It plays a critical role in calculating land surface emissivity, which is essential for accurately understanding surface temperatures and various Earth system processes. MODIS captures thermal infrared data through its emissive bands, particularly Band 31 and Band 32, which are highly sensitive to the thermal radiation emitted from Earth’s surface [31]. The instrument records radiance values in these specific spectral bands. To estimate LST and emissivity, MODIS uses a split-window algorithm that takes advantage of the different atmospheric absorption properties of its two thermal bands. This method allows for the effective separation of atmospheric effects from the actual ST [32]. In this approach, emissivity values for Band 31 and Band 32 are estimated based on land cover classification, atmospheric water vapour content, and near-surface temperature, which are divided into manageable sub-ranges to optimise retrieval accuracy. Additionally, MODIS uses a day/night algorithm to retrieve both daytime and nighttime LST and surface emissivity values from paired observations across seven TIR bands. The resulting data products include LST measurements, QAs, observation timestamps, sensor viewing angles, and emissivity estimates. MODIS emissivity data are extensively used in climate modelling, weather forecasting, and environmental monitoring. These datasets provide valuable insights into surface energy balance, vegetation condition, and land cover dynamics.

Several algorithms have been integrated into software tools to support LST retrieval workflow [10]. For instance, a plugin developed for the open-source software QGIS v3.4.3 enables users to calculate LST from Landsat 5 and 7 imagery [33]. Similarly, dedicated tools have been developed for ERDAS v16.8 software to process Landsat 8 thermal data. However, these tools typically require local installation, as well as the downloading and preprocessing of raw satellite imagery. Tasks that can be both time-consuming and computationally demanding. It is also noteworthy that the USGS has announced plans to release LST data products covering the entire Landsat archive. However, as of now, these products are not yet publicly available.

The RSLab conducts research across both urban and natural environments, with a particular emphasis on urban climate and spatial planning. Its work focuses on climate change mitigation, adaptation strategies, and environmental monitoring to enhance ecosystem services and support risk management related to natural and technological hazards. The RSLab applies advanced machine learning techniques to analyse Big Data from global satellite missions, developing localised applications using cloud-based infrastructures. A multi-sensor approach is used to estimate energy, water, and carbon fluxes between the land surface and the atmosphere. One of RSLab’s tools is an interactive online map that visualises LST calculations derived from multiple satellite sources, including Landsat 4, 5, 7, and 8, as well as ASTER and MODIS. The system also incorporates NDVI-derived emissivity products to enhance the accuracy of thermal assessments [10,11]. The SC algorithm developed by Jiménez-Muñoz et al. [10,13] is widely preferred for its simplicity, as it requires only one thermal infrared band (Band 10) to estimate LST. The SC algorithm uses satellite-acquired thermal infrared data and applies atmospheric corrections to account for the influence of atmospheric conditions on the observed temperature. One of the main challenges in applying the SC algorithm lies in accurately correcting for atmospheric transmission and surface emissivity. Two factors that can introduce significant errors into LST estimates. As a result, several modifications and enhancements to the original SC algorithm have been proposed to improve its accuracy and robustness. For example, the Practical Single-Channel (PSC) algorithm was introduced to reduce errors by avoiding the linearisation of the Planck function and improving the accuracy of atmospheric correction [34]. An alternative implementation of the SC algorithm is accessible through Google Earth Engine, which utilises data from Landsat Thematic Mapper (TM), Landsat Enhanced Thematic Mapper Plus (ETM+), and Operational Land Imager (OLI) with TIRS sensors to retrieve LST and other multispectral indices [35].

3. Results

LST was estimated using NDVI-based values through the system developed by RSLab for Landsat thermal data. Tracking NDVI dynamics offers critical insights into vegetation health and the effects of environmental changes on ecosystems. To maintain water quality, sustainable environmental strategies should prioritise the preservation of vegetation cover and the prevention of excessive eutrophication. In Lake Drūkšiai, NDVI values ranged from 0.05 to 0.919. Figure 5 provides a representative example (from 2020) of NDVI indices, illustrating the spatial distribution of vegetation density.

The coastal zone of Lake Drūkšiai predominantly exhibited NDVI values between 0.48 and 0.62, highlighted in yellow, indicating areas of moderate vegetation density. Pixels with NDVI values above 0.5 are considered fully vegetated, reflecting healthy coastal vegetation; however, such values may also signal ongoing swamping or eutrophication processes. In contrast, pixels with NDVI values below 0.20 correspond to bare soil or open water, indicating sparse or absent vegetation.

The NDVI values shown in Figure 5 are presented with two significant digits to ensure clarity and to prevent overinterpretation of data precision. This level of detail is appropriate given the spatial resolution and radiometric sensitivity of Landsat 8 imagery. While NDVI is a reliable indicator of vegetation density, its values are influenced by natural variability and sensor limitations; therefore, the displayed values should be interpreted as approximate estimates rather than precise measurements.

Ongoing monitoring of NDVI dynamics is essential for effective environmental management and for safeguarding aquatic ecosystems against the long-term impacts of industrial activities.

LST was calculated using the system developed by the RSLab for processing Landsat thermal data [10]. LST is a critical parameter for analysing the thermal behaviour of land–water interfaces, which are influenced by vegetation cover, water bodies, and human activities. A summary of the results is presented in Figure 6.

The LST values presented in this study were derived from Landsat 8 thermal infrared imagery using established retrieval algorithms. While the sensor’s spatial resolution and calibration enable reliable ST estimation, the typical accuracy of LST retrievals is within ±1–2 °C. Accordingly, the temperature values shown in Figure 6 have been rounded to reflect appropriate precision. These values should be interpreted as approximate representations of surface thermal conditions, rather than exact measurements.

It is important to note that areas displaying 0 °C values in Figure 6 do not reflect actual surface temperatures. These values indicate locations where LST data were unavailable due to cloud cover during image acquisition. The colour scale was adjusted to clearly differentiate these “no data” zones from valid temperature readings.

Randomly selected survey points along the coastline of Lake Drūkšiai were used to analyse temperature trends (Figure 7). Detailed temperature measurements for these points are provided in Supplementary Materials (Table S1).

However, due to extensive cloud cover, LST values could not be retrieved for several survey points (specifically, points 20, 41, and 42). Additionally, points located within Belarusian territory (points 51 to 60) were excluded from the analysis.

The LST results for 2013 revealed comparatively lower surface temperatures than in later years, likely reflecting residual cooling effects following the recent decommissioning of the INPP. By 2020, a noticeable increase in LST was observed, suggesting the emergence of a regional warming trend, potentially driven by broader climatic factors. Elevated temperatures persisted in 2023, reinforcing this trend. The 2024 results indicated slight stabilisation, yet temperatures remained above 2013 levels, suggesting that the thermal regime has not reverted to pre-INPP conditions. A general overview of temperature trends at the surveyed points is presented in Figure 8.

According to studies by Mačiulionienė et al. [17], the coastal water zone of Lake Drukšiai can be classified into three categories:

• Areas distant from the INPP;
• Cooling water intake zones;
• Technological water discharge zones.

Environmental specialists at the INPP continue to monitor water temperature at six designated locations (E1–E6), as illustrated in Figure 9.

These measurement points are strategically positioned to capture data from distinct thermal impact zones, including technological discharge areas and cooling water intake zones. The recorded temperatures at these locations can be compared with LST values derived from Landsat 8 imagery, thereby supporting the validation of remote sensing observations.

For instance, Figure 8 illustrates the low temperatures recorded at survey points 4 and 32 on 5 May 2013 (point 4: 10.31 °C; point 32: 12.42 °C), both of which were located farther from the coastline. These low values are consistent with expectations based on their geographical location. In subsequent years, elevated temperatures were observed in the technological water discharge zones, particularly near the E5 monitoring point, which includes points 12, 13, 18, and 19 (see Figure 10).

Table 1. Comparative surface temperature data (°C) from INPP measurement point E5 and nearby survey points (2013–2024).

	Temperature, °C
Point	5 May 2013	5 June 2015	3 May 2018	18 June 2020	11 June 2023	16 August 2024
E5	19	19	18	26	19	22
12	20	22	18	25	22	24
13	16	18	16	24	19	23
18	16	22	20	26	23	21
19	15	22	20	27	24	22

summarised the comparative temperature data from in situ measurements at E5 and Landsat-derived estimates for these points.

The results confirm an increase in ST in this area. However, according to information provided by the INPP, the outlet channel is currently inactive and no longer discharges water into Lake Drūkšiai. Therefore, the observed temperature fluctuations are more likely attributed to broader climatic changes and ongoing eutrophication processes within the lake. We recommend that future research explore the potential drivers of localised temperature anomalies in this zone, including climatic, ecological, and hydromorphological factors.

Survey points 29 and 30 showed a clear trend of lower temperatures, particularly in 2018 (point 29: 12.08 °C; point 30: 15.67 °C; see Supplementary Materials and Figure 8). An examination of the coastal environment near these locations revealed the presence of a river inflow, which likely contributes to the localised cooling effect. This suggests a natural thermal modulation resulting from the river’s input.

Survey point 31 is situated near a residential area. According to environmental protection regulations, residents are permitted to discharge biologically treated wastewater into natural water bodies. However, it is important to verify whether any technological water discharges in this area comply with biological treatment standards.

Survey points 37–40, located furthest from the INPP, are also situated near residential areas. An interesting observation at point 39 was the presence of a large reinforced concrete pontoon used for launching watercraft into the lake. Temperatures recorded at this location were consistently lower than the lake’s average, although the specific factors contributing to this cooling effect remain unclear.

Survey points 63–67 are located within the technological water discharge zones. It is encouraging to observe that water temperatures in these areas have stabilised and do not exceed the average LST values recorded between 2020 and 2024.

The average LST values along Lake Drūkšiai’s coastline for six selected years between 2013 and 2024 were derived from Landsat 8 thermal infrared data and are presented in Figure 11. The error bars represent the standard deviation, indicating the variability of temperature measurements across different coastline locations.

The results show a clear upward trend in average LST over time, with the highest values recorded during the summers of 2020 and 2024. This trend further supports the hypothesis that the lake’s thermal regime has not reverted to pre-industrial conditions, despite the INPP closure in 2009. The relatively high standard deviation observed in 2020 indicates increased spatial variability, possibly driven by localised thermal anomalies or broader climatic influences.

4. Discussion

The analysis of the LST trends along the coastline of Lake Drūkšiai offers valuable insights into the lake’s post-industrial thermal dynamics. Despite the decommissioning of the INPP in 2009, the lake’s coastal thermal regime continues to exhibit elevated temperatures compared to pre-INPP levels. Although 2024 LST data indicate slight stabilisation, temperatures remain above those recorded in 2013, suggesting that full recovery to pre-industrial conditions has not yet occurred. These findings underscore the lasting influence of historical thermal discharges and imply that additional factors, such as localised anthropogenic activities or broader climatic changes, may also be shaping the current thermal regime. Ongoing monitoring of coastal zones is essential for detecting emerging pollution sources and preventing further ecological degradation. Although the remote sensing methodology employed in this study effectively captures surface temperature variations, in situ validation remains critical to ensure accurate interpretation and to detect potential illegal discharges or other human-induced impacts.

Figure 8 illustrates temperature trends across various shoreline survey points. However, the data do not show a consistent pattern of increase or decrease over time. The temperature values fluctuate between higher and lower readings across different years, limiting the ability to identify a clear long-term trend. This variability may be influenced by seasonal effects, cloud cover during image acquisition, and localised environmental conditions. Moreover, although randomly selected survey points were used to assess shoreline temperature dynamics, direct comparison with in situ measurements (Figure 9) is limited. The INPP monitors only six fixed points (E1–E6), and due to cloud cover, satellite-derived LST data were unavailable for several survey locations, reducing the spatial overlap needed for robust validation and trend analysis.

Recent advancements in remote sensing technologies, as demonstrated by Bonansea et al. (2021) and Wei et al. (2023) [7,9], have validated the use of Landsat imagery combined with machine learning for monitoring water surface temperatures and detecting thermal plumes from nuclear power plants. The present study is consistent with these approaches, confirming the effectiveness of Landsat 8 imagery and NDVI-based LST models for monitoring long-term thermal trends in aquatic environments.

Similarly, Šarauskienė (2002) documented a noticeable rise in the average temperature of Lake Drūkšiai during the INPP’s operational period [19]. This study builds upon earlier findings by demonstrating that, even 15 years after the INPP’s closure, the lake’s thermal regime has not fully recovered. This underscores the persistent and long-term influence of industrial thermal pollution. Marčiulionienė et al. (2011) also reported changes in the macrophyte and fish communities in Lake Drūkšiai, linked to temperature alterations caused by the INPP’s cooling processes [17]. The current findings further support the conclusion that industrial activities have lasting impacts on aquatic biodiversity and ecosystem health.

These findings align with previous research on the effects of industrial activities on aquatic ecosystems. For example, Kirillin et al. (2013) highlighted the significant impact of thermal pollution from nuclear facilities on lake temperature dynamics and mixing regimes [18]. Their study demonstrated that thermal discharges can substantially alter a lake’s thermal structure, which corresponds with the temperature fluctuations observed in Lake Drūkšiai.

From a land change perspective, these findings underscore how legacy industrial infrastructure can leave enduring thermal and ecological imprints on surrounding landscapes, highlighting the importance of spatially explicit monitoring frameworks.

5. Conclusions

The persistent thermal anomalies observed in Lake Drūkšiai highlight the need for continuous, integrated monitoring approaches that combine satellite-based observations with ground-truth verification. Future research should aim to identify and quantify previously undetected sources of thermal pollution and to develop effective mitigation strategies to ensure the resilience and sustainability of aquatic ecosystems under changing environmental conditions.

Ensuring the sustainability of aquatic ecosystems requires ongoing vigilance, continued refinement of monitoring technologies, and the implementation of proactive environmental management practices to protect these ecosystems from the long-term consequences of industrial activities.

The successful use of NDVI-based LST models, combined with Landsat 8 imagery, emphasises the importance of integrating advanced remote sensing techniques with field validation to track and manage the thermal dynamics of water bodies.

The division of coastal zones into remote areas, cooling water intake zones, and technological water discharge zones enhances the understanding of spatial thermal variations and supports targeted environmental management strategies.

Quantitative results show that LST values in 2024 remained elevated, with average temperatures in discharge zones reaching up to 27 °C, compared to 10–12 °C in remote areas in 2013. NDVI values ranged from 0.05 to 0.92, with coastal zones typically showing moderate vegetation density (NDVI 0.48–0.62), indicating ongoing ecological processes such as swamping or eutrophication. These findings confirm that, even 15 years after the INPP’s closure, the lake’s thermal regime has not returned to pre-industrial conditions.

Such numerical evidence reinforces the conclusion that legacy industrial impacts persist in land–water systems and highlights the value of geospatial monitoring for long-term land change assessment.

Given the strategic importance of Lake Drūkšiai as a former cooling reservoir for the INPP, the findings underscore the urgent need for local environmental authorities to institutionalise long-term thermal monitoring. Integrating satellite-based LST data into regional environmental policy frameworks would enhance early detection of ecological risks and support evidence-based decision-making for the sustainable management of this nationally significant water body. Although MODIS data offers valuable insights for large-scale thermal trend analysis, its spatial resolution of 1 km is not well-suited for detailed nearshore studies such as those conducted in Lake Drūkšiai. The narrow coastal zones and in situ monitoring points used in this research often fall within areas smaller than a single MODIS pixel, limiting the accuracy and relevance of direct comparisons. Therefore, MODIS is referenced only as a supplementary tool for broader seasonal anomaly detection, and its limitations in spatial precision are acknowledged. For shoreline-specific analysis, higher-resolution sensors such as Landsat or Sentinel-2 remain essential.

Beyond the specific findings for Lake Drūkšiai, this study demonstrates the broader applicability of remote sensing techniques for post-industrial landscape monitoring. The integration of NDVI-based LST analysis with long-term satellite data provides a replicable methodological framework for assessing legacy thermal pollution in inland water bodies. Such approaches are particularly valuable for transboundary lakes, where consistent, scalable, and non-invasive monitoring tools are essential for coordinated environmental governance. The results also highlight the potential of remote sensing to support evidence-based decision-making in the context of long-term ecological recovery and sustainable land–water interface management.

Future research could integrate MODIS data to complement Landsat-based analysis, particularly for broader-scale monitoring and early warning of thermal shifts. MODIS provides daily and 8-day composite LST/emissivity data at a 1 km × 1 km spatial resolution, which are highly valuable for large-scale thermal trend analysis. However, in the context of this study, the INPP’s in situ monitoring points are located in narrow coastal zones, and their spatial extent is often smaller than a single MODIS pixel. This makes a direct comparison between MODIS-derived LST values and ground-based measurements challenging in this specific case. Nevertheless, MODIS data could be used for initial thermal anomaly detection and seasonal modelling in large water bodies. Once anomalies are detected, higher-resolution sensors such as those on Landsat or Sentinel-2 would still be necessary for detailed spatial analysis and validation.

Based on the observed long-term thermal anomalies, future research should focus on modelling the ecological consequences of elevated water temperatures, analysing the influence of land use and river inflows on shoreline thermal dynamics, and developing integrated monitoring frameworks that combine satellite data with in situ ecological indicators to support adaptive environmental management.