Analysis of DInSAR Deformation Changes to Land Surface Temperature Due to Mount Semeru Eruption in 2022

The monitoring of disasters is carried out through various methods, depending on their nature and the effects they have on the surrounding area, including the monitoring of volcanic hazards, which differs from the monitoring of other disasters such as landslides, tsunamis, and floods [1]. The discharge of heavy materials, spesifically pyroclastic flows and ashfall, causes devastating consequences for the the environment and society, resulting in ruined crops, destroyed property, and loss of life [2]. They are one of the natural disasters that claim the most victims besides earthquakes [3].There are 4 (four) stages to define the status of an active volcano: normal (level I), alert (level II), standby (level III) and warning (level IV). Thus, continuous and real-time observation are required to decide the state of a volcano, especially when eruptive activities are increased [4], [5].

Mount Semeru is geographycally located at 8$^\circ$06$^\prime$30$^{\prime \prime}$ S and 112$^\circ$55$^\prime$00$^{\prime \prime}$ E , in the area of Lumajang District and Malang Regency, East Java [6]. Mount Semeru is the third highest mountain on the Java Island with its cone reaches height about ±3676 meters above sea level and has an active crater called the Jonggring Seloko [7], [8], [9].

Geographic Information System (GIS) is a type of system or software which can input, manage, manipulate, summarize, update, store and analyze geographical data [10], [11]. This software processes remote sensing data, enabling the measurement and observation of objects or phenomena from a distance without direct interaction [12], [13], [14].

Sentinel imagery is a satellite designed to transmit data to the Copernicus program, where the program monitors daily changes on earth’s surface, including environments and climates. Sentinel imagery has several satellites which are designed to operate according to their specific purposes, such as Sentinel-1, Sentinel-2, Sentinel-3, and Sentinel-5P [15]. For example, Sentinel-1 presented in Figure 1 is useful to observe land deformation, sea breezes, icebergs,etc.

Synthetic aperture radar (SAR) is an efficient tool for Earth’s surface analysis. SAR data are essential various scientific researches [14], [17], [18]. In obtaining the distance and altitude of an object on Earth’s surface, SAR combines combines two or more interferometry data. This information is based on the value of phase changes which then converted into visual shift line-of-sight (LOS) as a signs of separation or information [19], [20].

Differential Interferometric Synthetic Aperture Radar (DInSAR) is a radar-based technique that uses phase information from at least two SAR data obtained over the same area at different times. The purpose is to obtain the deformation measurements over time in an area [21], [22], [23]. DInSAR is the developments of InSAR method which generates displacement maps as a result of changes in deformation [24], [25], [26], [27], [28].

Dinsar aims to observe shifts or deformations of the ground using the repeat-pass interferometry approach [21]. The changes in the interferometric phase include information from the topographic profile ($\varphi$topo), variations in the orbital path ($\varphi$orb), ground deformation ($\varphi$defo), atmospheric effects ($\varphi$atm), and phase noise ($\varphi$noise), which can be expressed as follows [29].

where,

$\Delta \varphi$ = Phase difference between two SAR images,

$\varphi$topografi = topographic or terrainphase,

$\varphi$orbit = to ensure that differences in satellite positions do not result in errors when measuring ground surface movement,

$\varphi$deformation = deformation phase,

$\varphi$atmosphere = atmospheric-influenced phase caused by changes in atmospheric conditions, such as water vapor,

$\varphi$noise = random noise phase.

Several methods are employed to eliminate or mitigate the effects of satellite orbit, terrain, noise, and other factors that can distort DInSAR images. The specific methods used are as follows:

1. Terrain effect (Topography)

Problem: Terrain variations, such as elevation changes, can cause phase shifts in the interferogram that do not correspond to actual surface deformation but rather to changes in topography.

2. Satellite orbit

Problem: Inaccurate satellite orbit data can cause errors in phase measurements, as the position of the satellite during data acquisition may not be precisely known.

3. Deformation

Problem: Surface deformation due to natural events such as earthquakes or volcanic activity can result in phase changes in the interferogram. However, distinguishing deformation from other sources of phase shifts can be challenging.

4. Atmospheric effects

Problem: Atmospheric conditions, such as variations in pressure, humidity, and temperature, can alter the radar signal, leading to phase changes that are not related to surface deformation.

5. Noise

Problem: Noise, including random errors from sensor limitations or external interference, can corrupt DInSAR images, causing false phase variations that do not reflect real surface changes.

In summary, this article employs a combination of methods, such as the use of Digital Elevation Model (DEM) for topographic corrections, precise satellite orbit data, deformation models, temporal filtering, phase unwrapping, and noise filtering, to mitigate the effects of terrain, orbit inaccuracies, atmospheric influences, and noise on DInSAR images. These techniques ensure that the final data provides an accurate representation of surface deformation.

Sentinel-3 presented in Figure 2 is a satellite designed to monitor large-scale global dynamics to provide real-time useful information in weather forecasting by measuring temperature, color, and sea level.

Land surface temperature (LST) is one of the data acquisition techniques to define Earth’s surface temprerature, an indicators associated with natural phenomena and climate change [31]. LST is influenced by many factors, such as land surface type, surface moisture, illumination, and atmospheric conditions [31], [32]. The Sentinel-3 satellite images and sea and land surface temperature radiometer (SLTR) instrument are important components of the Copernicus monitoring system , developed by the European Space Agency in providing thermal data at morning and night observation time [33], [34], [35], [36].

Deriving LST from thermal radiation measured at the Top of Atmosphere (TOA) requires first eliminating the effects of atmospheric interference and emissivity.

where,

$a_0, b_0, c_0=$ The class of coefficients applied to brightness temperature that depends on water vapor in the atmosphere, satellite viewing angle, and surface emissivity.

$T_{11}, T_{12}=$ Brightness temperature on $T_{11}$ and $T_{12}$.

Raster calculator can perform mathematical or logical operations between the datasets used for image analisys. This operation requires the image layer to be analyzed, then a formula is entered to perform the calculation. After processing, the result will appear as s new image. The raster calculator allows rapid data processing without the need for complicated manual handling. Raster calculator supports mathematical, logical, and statistical operations, enabling more complex data analysis and concise data processing such as mapping. Generally, satellite images of the Earth’s surface temperature are measured in Fahrenheit (F), so the coversion process (K − 273.15) is needed to obtain the temperature in Celcius (℃) [34].

This study aims to determine the relationship between the changes in the DInSAR deformation and LST in around Mount Semeru, East Java at the eruption in 2022. Analysis on the DinSAR deformation and LST are focused on the area of Mount Semeru, East Java. Mount Semeru is known for its cone-like shape and classified as a stratovolcano. On December 4, 2021, an explosive eruption occured and produced a considerable pyroclastic clouds, lahars, and ash fall, resulting in fatalities and infrastructure damage. Indonesia National Agency of Hazard Mitigation or National Disaster Management Agency (Badan Nasional Penanggulangan Bencana, BNPB) reported that this eruption activity forced 9,977 people to evacuate. Exactly one year later, on December 4, 2022, the volcano erupted again with its lahar flowed in different direction, that is towards the Glidik River, the main channel for lahar flows down the slopes of Mount Semeru. The results of this study are expected to be used as references or comparisons for further research or implementation of other related methods, especially for volcanic hazard mitigation.

This study is focused on an area around Mount Semeru with coverage roughly about ±1008.60479 km$^2$, as illustrated in Figure 3.

This research utilized data from Sentinel-1 satellite for SLC data (obtained from the Copernicus website: https://search.asf.alaska.edu/) and Sentinel-3 satellite for SLTR data (obtained from https://scihub.copernicus). The study focuses on Mount Semeru, located in East Java, Indonesia. Mount Semeru is an active stratovolcano, known for its frequent eruptions and complex volcanic activity.

The data covers images of Mount Semeru before, during, and after the eruption in 2022. Specifically, the data is divided into three periods: prior to the eruption (November 8−20, 2022), during the eruption (November 20–December 14, 2022), and after the eruption (December 14–26, 2022). Mount Semeru's eruption in 2022 was significant, with pyroclastic flows, ash fall, and lahars causing substantial damage to surrounding areas. These events led to fatalities and the displacement of local populations.

The Sentinel image data were processed using the SNAP Toolbox software, and the results were mapped using QGIS software to analyze land surface deformation. This study aims to provide insights into the changes in land deformation caused by the eruption and monitor the impact on the surrounding terrain.

There are several limitations in this study, which include:

1. Data source constraints: the research is confined to a certain timeframe, relying solely on imagery from Sentinel-1 and Sentinel-3, capturing events only around the eruption in 2022. Consequently, any changes that took place outside this timeframe cannot be examined.

2. Methodological constraints: the research employs a two-pass approach assuming minimal ground movement between the two image acquisitions. This assumption lead to inaccuracies if substantial displacement occurs, which might not be effectively captured by this technique.

3. Software or analytical constraints: the tools used for analysis, SNAP Toolbox and QGIS, are certainly beneficial; however, they do have limitations regarding the processing of extensive datasets, the precision of analysis dataset and the precision of analysis under specific circumstances.

Implementation steps for conducting data analysis through quantitative methods include:

1. Gathering images from the official Copernicus website to acquire Sentinel-1 and Sentinel-3 imagery.

2. Identifying high-quality images through processing tests to select appropriate images for research purposes.

3. Utilyze SNAP software for data processing, and then converting the processed outputs into maps using QGIS software.

4. Analysing on obtained the results.

The two-pass method for DInSAR deformation analyssis requires two sets of SLC data in the SAR interferometry process. This datasets includes satellite imaging data on the period before the eruption (November 8–20), during the eruptions (November 20–December 14) and after the eruptions (December 1–26).The DInSAR processing was used to filter images affected by curves, satellite orbits, topography, noise, and interferometry deformations. The coherence values obtained in the DInSAR process are in the range from 0 to 1. Value of 1 represents the same relationship between the master image and the slave image, and the value of 0 is the opposite. A phase decapsulation correction is implemented to convert the phase unit from radian to metric, while a geocoding correction is implemented to analyze the deformation in different processing method, namely the two-pass method. The final step after processing is to analyze the changes of DInSAR caused by volcanic eruptions.

LST analysis requires the data from Sentinel-3 (SLTR) level-2 LST and processed with SNAP TOOLBOX and GIS software. This study uses dataset at prior of the eruption (November 25, 2022), during the eruption (December 11, 2022), and after the eruption (December 17, 2022). The reprojecting process is performed as correction to align the image coordinates to the actual one. The image with corrected coordinates then saved in the GIS software in GeoTif format. Next, the temperature unit is converted from Fahrenheit to Celcius with a raster calculator (K − 273.15). Using extraction method on the raster calculator, the image is then cropped to obtain the information of the focused area. The temperature range obtained by adjusting the color grade of the image. The output of image processing in QGIS software is presented in a map of LST. The final step is to analyse the change of the temperature in the LST images.

All of the processing steps conducted in this study is illustrated in a flow chart in Figure 4.

The processing of DInSAR deformation generates both DEM and DInSAR data. A DEM, or digital elevation of the Earth’s surface. The DEM conveys details about the configuration of the land surface. In contrast, DInSAR is utilized to observe alterations in the shape of the Earth’s surface or land deformation. The outcomes of processing DInSAR deformation before, during and after the event of eruption of Mount Semeru at 2022 are shown in Figure 5, Figure 6 and Figure 7, respectively.

The research results indicate that the changes in DInSAR deformation shown on the negative scale bar is small or no change in DInSAR deformation. The DInSAR deformation map before the 2022 eruption of Mount Semeru is presented in Figure 5. Figure 5a displays the DEM, representing the deformation state, while Figure 5b illustrates the DInSAR deformation changes before the eruption. The scale bar in Figure 5b indicates deflation values ranging from -0.025 cm to -0.054 cm, represented by colors from yellow to red. Due to the difficulty in reading the deformation during the filtering and unwrapping process, empty pixels are left colorless, and phase values that are too small are removed. In Figure 5b, before the eruption of Mount Semeru in 2022, DInSAR deformation, a layer called lava deposit or lava tongue formed in crustal deformation caused by the eruption of 2021. When the lava flow from the Semeru crater moves to lowlands or valleys, cold and solidified lava causes changes in shape due to uneven cooling.

The presence of positive values in DInSAR deformation changes suggest a notable shift in deformation resulting from the activities of Mount Semeru, as well as other natural elements that can physically alter shapes or structures. The maps reflecting changes in DInSAR deformation during the 2022 eruption of Mount Semeru are illustrated in Figure 6. Figure 6a displays the DEM image of deformation changes, while Figure 6b depicts the variations of DInSAR deformation in which occured inflation throughout the eruption, with a scale indicating values ranging from 0.015 cm to 0.3 cm (marked by a transition from dark blue to light blue). The rise in deformation changes is attributed to Mount Semeru’s volcanic activity. Furthermore, the transition from dark blue to light blue in Figure 6b illustrates the changes in DInSAR deformation in areas where variations cannot be determined; these locations are uncolored because the phase values were minimal during the filtering and unwrapping process, leading to their exclusion from the color representation.

Figure 6b shows the DInSAR deformation recorded during the 2021 Semeru eruption, in which a layer known as a lava deposit or lava tongue formed as a result of the crust deformation of the eruption. The lava flowing from Semeru crater descends into the lowlands or valleys and causes the formation of lava tongue. The DInSAR images shown in Figure 6b show unclear deformation changes caused by the lava flow during teh Semeru eruption, as indicated by the red circle. The continuous motion of hot lava does not produce measurable DInSAR deformations.

Positive deformation changes observed in the results of this study indicate that significant changes in deformation are caused by activities of Mount Semeru and other organisms that can change a shape or structure in reality. Changes in the DInSAR deformation after the eruption of Mount Semeru in 2022 are presented in Figure 7. Figure 7b shows changes in the deformation after the eruption, with that scale bar value indicating inflation from 0.049 cm to 0.099 cm marked with dark blue to light blue. The pixels not colored due to the phase values during the filtering and unwrapping process are removed. Figure 7b illustrates the deformation recorded by DInSAR follo the 2022 eruption of mount Semeru, where a geological formation known as lava deposits or lava tongue formed as a result of crustal deformation from he previous eruption. The lava that was expelled from the Semeru crater moves into the lowlands or valleys, resulting in the creation of lava tongue. The DInSAR image presented in Figure 7b highlights these areas, indicated by a red circle, which are associated with the lava flow patterns from the Semeru eruption. It is important to note that hot lava flows do not produce detectable DInSAR deformation.

The LST result map before the eruption in 2022 (see Figure 8), indicates a minimum temperature of 18.6 ℃, marked in green, located in the eastern, southeastern and southern half of Mount Semeru. In contrast, the maximum temperature recorded was 27.8 ℃ in gold yellow, and it probably relates to previous volcanic activity or the 2021 eruption. The light blue area to the west and south of Mount Semeru represents cloud cover, with temperatures below 10 ℃.

The LST results map from the 2022 eruption of Mount Semeru (see Figure 9) shows that lowest temperature recorded was 16.9 ℃, indicated by green on the eastern part of the summit. Conversely, the highest temperature during the eruption reached 36.3 ℃, represented by a blackish-red color (marked by red circle), resembles a lava flow extending southeast from Mount Semeru. Additionally, the areas displayed in yellow with red patterns around the mountain’s slopes are considered remnants of living organisms or volcanic smoke, with a temperature registered at 29.8 ℃.

The LST map following the eruption of Mount Semeru in 2022 (see Figure 10) indicates a peak temperature of 33.6 ℃, highlighted in red on the map. The red circle denotes the highest temperature observed at the remaining lava flow from the eruption. The yellowish-red pattern surrounding the slopes of Mount Semeru is thought to be a result of human activity or remnants of the eruption, with a temperature of 25 ℃. The lowest temperature recorded post-eruption is 16.5 ℃, marked in green, and is located near the summit, extending towards the north of Mount Semeru. In addition, the areas covered by clouds are represented of below 10 ℃.

This research aims to analyze surface temperature changes and deformations occurring around Mount Semeru during the period before, during, and after the eruption that took place in 2022. The method used is the analysis of LST data from the Sentinel-3 satellite and surface deformation using the Sentinel-1 satellite through DInSAR technology. Based on the obtained results, significant differences were found in surface temperature and deformation, which can be explained by the volcanic activity of Mount Semeru.

Observations of surface temperature reveal that before the eruption, the peak temperature reached 27.8 ℃, while the lowest temperature stood at 18.6 ℃. During the eruption, the peak temperature rose significantly to 36.3 ℃, whereas the minimum temperature dropped to 16.9 ℃. Following the eruption, the maximum temperature saw a slight decrease to 33.6 ℃, and the minimum temperature further declined to 16.5 ℃.

The notable increase in the highest temperatures during the eruption can be explained by the onset of volcanic activity, where lava and other hot substances expelled from the volcano's crater elevate the surrounding surface temperature. The presence of lava igniting the ground leads to extreme temperature variations, which are effectively captured by the LST sensor on the Sentinel-3 satellite.

After the eruption, even though the highest temperature showed a slight reduction, it remained higher than the temperatures recorded before the eruption. This suggests that post-eruption volcanic activity continues to exert a significant thermal influence in the vicinity of the mountain, potentially due to lahars, hot volcanic debris, or alterations in vegetation and land cover resulting from the eruption. The decline in low temperatures after the eruption also indicates a natural cooling process in the area, although high temperatures are still elevated compared to pre-eruption levels, likely due to ecological recovery and changes in surface albedo.

The analysis of surface deformation using Sentinel-1 data reveals notable alterations in deformation figures throughout the eruption period. Before the eruption, the recorded deformation values ranged from -0.025 cm to -0.054 cm, suggesting a relatively stable subsidence of the surface. However, during the eruption, a significant shift in deformation was observed, with positive values between 0.015 cm and 0.3 cm, indicating upward movement or shifts in the surface attributed to volcanic activity, such as magma movement or pressure buildup beneath the crust.

Following the eruption, there was a minor reduction in the deformation values (between 0.049 cm and 0.099 cm), but the recorded figures continued to show positive deformation, denoting that volcanic activity and changes to the land surface are still in progress. This ongoing activity may be a result of the lava that has erupted and flowed, along with the adjustments of the Earth's crust instigated by the eruption, which can influence the geological framework and surface characteristics of the area.

In general, the variations in LST and surface deformation during the eruption of Mount Semeru demonstrate a strong correlation. As the eruption takes place, the surface temperature increases dramatically, which directly corresponds to the positive deformation resulting from the movement of magma and the pressure build-up beneath the surface. These two events influence one another, with the flowing lava leading to a significant rise in surface temperature, while the magma movement and pressure fluctuations beneath the ground induce deformation of the surface.

The observed increase in deformation during the eruption suggests that volcanic activity has an impact on the geological and topographical features surrounding the mountain. The eruption of molten magma triggers abrupt temperature shifts at the surface, which can signal the presence of shifts and fissures in the surface soil layers. Consequently, understanding the relationship between LST and deformation is crucial in the field of volcanology, as both offer insights into the dynamic processes occurring at Mount Semeru throughout the eruption period.

This research demonstrates that integrating LST data from Sentinel-3 with deformation data from Sentinel-1 can yield extensive insights into the thermal and structural conditions present during and following an eruption. LST is useful for tracking variations in surface temperature, while DInSAR can detect surface deformation that may not be readily apparent. The synergy of these two datasets enhances the precision of volcanic activity monitoring and its consequences on the surrounding ecosystem.

Moreover, the findings from this study can aid in disaster mitigation efforts, particularly in pinpointing regions most impacted by the eruption. A deeper comprehension of changes in surface temperature and deformation can support relevant authorities in their planning.

This study uncovers important results regarding the alterations in DInSAR deformation and LST that took place following the 2022 eruption of Mount Semeru. Before the eruption, the DInSAR deformation change map indicated fairly stable values, varying from -0.025 cm to -0.054 cm. Concurrently, the ground surface temperature recorded a low of 18.6 ℃ and a high of 27.8 ℃, indicating the typical conditions of Mount Semeru while it was in an inactive phase.

During the volcanic eruption, DInSAR deformation observations indicated a positive change (inflation), ranging from 0.015 cm to 0.3 cm, highlighting considerable volcanic activity. Throughout this timeframe, the ground surface temperature rose sharply, reaching a peak of 36.3 ℃, which aligned with the eruption of lava and other volcanic materials from the crater. This temperature increase is directly associated with volcanic activity, as the molten lava generates heat at the surface.

Following the eruption, the DInSAR deformation measurements varied from 0.049 cm to 0.099 cm, suggesting ongoing residual deformation related to the leftover lava. Additionally, ground surface temperatures decreased, peaking at 33.6 ℃. The more distinct deformation seen post-eruption suggests that the moving lava offers a clearer view of changes in surface shape, as there are no longer any hindrances affecting the analysis.

The results suggest that before the eruption, Mount Semeru was in a stable state with little deformation. During the eruption, the release of lava drastically changed the deformation pattern of the mountain and led to a significant rise in surface temperature. Following the eruption, the alterations in deformation are mainly driven by the residual lava, which facilitates clearer monitoring of changes in surface shape. The most notable deformation is seen around the crater and along the paths of the lava flow that continued to move during the eruption.

For future research, further data collection, the dataset used in this research covers a limited period (from November to December 2022), suggesting that future studies could extend the period of analysis to track long-term changes after the eruption and to recognize ongoing changes in the land. Incorporating data from more stellites or other types of sensors can also improve the accuracy of the results. And collaboration with field data, to confirm the findings obtained from satellite image analysis, upcoming research can combine field data such as GPS readings or other methods.