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Article

Mapping Surface Deformation in Rwanda and Neighboring Areas Using SBAS-InSAR

by
Adrien Mugabushaka
1,2,3,4,
Zhenhong Li
1,3,5,6,
Xuesong Zhang
1,3,4,
Chuang Song
1,3,5,6,*,
Bingquan Han
1,3,4,
Bo Chen
1,3,4,
Zhenjiang Liu
1,3,4 and
Yi Chen
1,3,4
1
College of Geological Engineering and Geomatics, Chang’an University, Xi’an 710054, China
2
Department of Civil Engineering, Rwanda Polytechnic (RP-Ngoma College), Kibungo P.O. Box 35, Rwanda
3
Key Laboratory of Loess, Xi’an 710054, China
4
Big Data Center for Geosciences and Satellites, Chang’an University, Xi’an 710054, China
5
Key Laboratory of Western China’s Mineral Resources and Geological Engineering, Ministry of Education, Xi’an 710054, China
6
Key Laboratory of Ecological Geology and Disaster Prevention, Ministry of Natural Resources, Xi’an 710054, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2024, 16(23), 4456; https://doi.org/10.3390/rs16234456
Submission received: 26 September 2024 / Revised: 3 November 2024 / Accepted: 19 November 2024 / Published: 27 November 2024
Browse Figures
Versions Notes

Abstract

:
Surface deformation poses significant risks to urban infrastructure, agriculture, and the environment in many regions worldwide, including Rwanda and the neighboring areas. This study focuses on surface deformation mapping and time series analysis in Rwanda and the neighboring areas from 2 July 2016 to 8 June 2023 using the Small Baseline Subset Interferometric Synthetic Aperture Radar (SBAS-InSAR). The influence of atmospheric delay error is effectively reduced by integrating the Generic Atmospheric Correction Online Service (GACOS), which provides precise atmospheric delay maps. Then the SBAS-InSAR method is utilized to generate surface deformation maps and displacement time series across the region. The results of this study indicated that the maximum deformation rate was −0.11 m/yr (subsidence) and +0.13 m/yr (uplift). Through time series analysis, we quantified subsidence and uplift areas and identified key drivers of surface deformation. Since subsidence or uplift varies across the region, we have summarized the different deformation patterns and briefly analyzed the factors that may lead to deformation. Finally, this study underscores the importance of SBAS-InSAR for tracking surface deformation in Rwanda and the neighboring areas, which offers valuable perspectives for sustainable land utilization strategizing and risk mitigation.

1. Introduction

Surface deformation is a phenomenon of land subsidence and uplift, is a critical geological risk that impacts the stability of land surfaces, and poses significant challenges to metropolitan and rural areas worldwide [1,2,3]. It is attributed to various factors, both natural forces and human activities [4,5]. Land deformation driven by natural forces involves loess collapse, consolidation of soil, soil erosion, compression of sediments, volcanic eruptions, and crustal movements [4,6,7]. However, human activities, such as excessive groundwater extraction, land reclamation, and engineering construction, can cause significant surface deformation over a short period [6,8]. Moreover, the relationship between different human activities and land deformations is severely understudied [9,10].
Traditional deformation monitoring methods, such as levelling and GNSS, have shown many limitations, such as rough spatial resolution and the higher cost in measuring and tracking land deformation in specific areas [11,12]. They are time-intensive, and their reliance on relatively few and widely dispersed monitoring stations makes them less suitable for monitoring large-scale and long-term deformations.
Since the late 20th century, Synthetic Aperture Radar Interferometry (InSAR) has emerged as a powerful remote sensing geodetic technique [13]. InSAR offers extensive spatial coverage and high spatial resolution while providing millimeter-scale precision [14]. It has been effectively employed in numerous studies and has proven its efficacy in monitoring large-scale deformations from space. Since 2014, The Sentinel-1 satellite, with its continuous monitoring capabilities, high-resolution imaging, short revisit periods, and open data policy, has significantly advanced the field of DInSAR technology. It has enabled the monitoring of surface deformations with unprecedented detail and frequency, making it an invaluable tool for a wide range of applications, from urban planning and disaster risk reduction to environmental monitoring and seismic studies [15,16]. The satellite’s multi-mode imaging further enhances its versatility, allowing it to adapt to various monitoring needs and environmental conditions. The Sentinel-1 satellite’s ability to capture deformations, along with its coverage of both azimuth and range directions, demonstrates its increasingly vital role in the field of Earth observation and geosciences. Nevertheless, the conventional InSAR technique faces several challenges in accurately measuring ground deformation, including spatial or geometrical decorrelation, temporal decorrelation, and atmospheric disturbances [17,18].
In response to these issues with the traditional method, various research institutions have developed and proposed advanced InSAR time series analysis methods that utilize multiple interferograms. InSAR technology can obtain the information of surface deformation with uncertainties ranging from millimeters to centimeters [19,20]. The Small Baseline Subset Interferometric Synthetic Radar (SBAS-InSAR) offers a novel approach to detect and analyze land subsidence over large areas and extended periods [21,22,23,24]. SBAS-InSAR is known for its ability to detect and measure ground deformations with high spatial-temporal resolution. However, atmospheric conditions may potentially lead to inaccuracies in these measurements. GACOS emerged as the best tool to overcome atmospheric conditions-related issues, providing precise atmospheric corrections, and the accuracy of SBAS-InSAR has significantly improved. Meanwhile, integrating GACOS helps to mitigate the atmospheric delay’s effects for more reliable ground displacement measurements to be obtained when applying SBAS-InSAR [18,25,26].
Rwanda’s unique geographical and geological characteristics make it an interesting case study for surface deformation analysis. The country, known for its stunning landscapes and numerous hills, is vulnerable to various natural phenomena, including volcanic activity, soil erosion, and landslides [27]. Moreover, Rwanda’s increasing urbanization and agricultural expansion have raised concerns about land degradation and potential subsidence in densely populated areas [28,29]. This study aims to shed light on these issues by leveraging SBAS-InSAR technology to monitor land subsidence patterns across Rwanda’s diverse landscapes. Broadly, the Kivu GNet network has proven helpful for deformation monitoring in the Kivu region in Central Africa. In particular, the network has observed inter-eruptive deformation compatible with currently ongoing magma accumulation under the Nyamulagira and Nyiragongo volcanoes and plate spreading across the western branch of the East African Rift System. Additionally, the network has recorded transient deformation from the 2010 and 2011–2012 Nyamulagira eruptions and the August 7, 2015, Mw5.8 Lwiro earthquake [30]. Previous studies conducted in Rwanda predominantly focused on evaluating the hazards associated with land movement using visual satellite images, GIS, and statistical modeling [31,32]. InSAR has also been introduced to measure land motion in Rwanda, such as the monitoring of earthflows in central Rwanda [33]. However, there are very few such InSAR-based deformation monitoring studies in Rwanda, and there is a research gap for studies at the national scale.
This study utilizes the SBAS-InSAR method to process 245 ascending-track Sentinel-1 images from July 2016 to June 2023 to monitor and analyze ground deformation patterns. These data have been processed through time series analysis to extract information about surface deformation in Rwanda and abroad over a specified time. The aim is to provide a deeper understanding of the causes and the spatial extent of land subsiding and uplifting phenomena across different regions.

2. Study Area and Datasets

2.1. Study Area

Rwanda is a landlocked nation situated in Central Africa. Rwanda, also known as ‘The Land of a Thousand Hills’, includes five volcanoes, 23 lakes, and countless rivers, some of which feed the Nile. The country is located 75 miles south of the equator in the Tropic of Capricorn, 880 miles ‘as the crow flies’ west of the Indian Ocean, and 1250 miles east of the Atlantic Ocean, putting it squarely in the heart of Africa. The elevation varies between 950 m and 4500 m above sea level. Rwanda is bounded by Uganda in the north, Tanzania to the east, Burundi to the south, and the Democratic Republic of the Congo to the west (see Figure 1).

2.2. Datasets

The Sentinel-1 satellites, which consist of a pair of satellites (Sentinel-1A and Sentinel-1B) working together, were created through the European Space Agency (ESA) as a component of the Copernicus Global Observing Program (CGOP). Table 1 shows the details of the SAR images used in this study.
We obtained a dataset comprising 245 Sentinel-1A/B ascending SAR images from 2 July 2016 to 8 June 2023 (see Table 1). We also acquired the exact orbital parameters corresponding to each dataset, collected from the ESA website (https://s1qc.asf.alaska.edu/aux_poeorb/) (accessed on 17 June 2023). Note that the Sentinel-1B satellite ceased operations as of 23 December 2021, so the Sentinel-1 images after 2022 are not as dense as before.
All possible interferometric pairs were generated using the strategy of small baseline subsets [20], with a maximum temporal baseline of 48 days. Finally, 939 interferograms were generated. Figure 2 shows the spatial and temporal baseline plot of the interferograms used in the displacement inversion.

3. Method

3.1. SBAS-InSAR Processing

This section presents the method used to determine land deformation with the InSAR technique (see Figure 3).
In this study, all SAR images are co-registered with the master SAR image and then paired sequentially. Due to the efficient coverage and excellent orbit control of Sentinel-1, a SAR image is set to be connected to up to four subsequent SAR images without requiring spatial baselines to meet the requirements of a small baseline subset [34,35]. Shuttle Radar Topographic Mission Digital Elevation Model (SRTM DEM) is an external DEM that removes the flat and topographic effects. A 4-pixel and a 20-pixel operation for range and azimuth direction were performed to suppress noise. The coherence threshold is 0.4, which means that points with coherence below 0.4 are permanently deleted before unwrapping. After generating the interferograms, the minimum cost flow (MCF) algorithm based on Delaunay triangulation was used for phase unwrapping to form the original interferograms [36,37]. GACOS was employed to generate atmospheric correction to all interferograms. Then, all interferograms are input into the SBAS-InSAR algorithm to generate cumulative deformation and deformation velocity.

3.2. GACOS Correction

GACOS, http://www.gacos.net/ (accessed on 10 August 2023), employs the Iterative Tropospheric Decomposition (ITD) model to differentiate between stratified atmosphere and turbulent atmosphere signals within the overall atmospheric interference, delivering near real-time global coverage for rectifying atmospheric interference inaccuracies in InSAR measurements (see Figure 4) [38].
Given the substantial topographic variations in the study region’s landscape, the prevalence of severe convective weather patterns, and the notable influence of atmospheric factors on InSAR interferograms, we employed the GACOS-supported InSAR technique [39,40], as shown in Figure 4.

4. Results

4.1. Deformation Velocity Determination

All 939 interferograms were generated and atmospheric interference corrected using GACOS, followed by cumulative deformation and linear mean velocity maps obtained using the SBAS algorithm. Linear mean velocity is the land surface deformation over a specific period, typically measured in meters per year. The results estimated the movement of the ground along the satellite’s line-of-sight (LOS) direction. In Figure 5, movements toward the sensor are positive (indicated by blue in the deformation maps), and movements away from the sensor are negative (represented in red).
The results, in Figure 5, reveal notable ground deformation, with a velocity ranging from −0.11 m/yr to +0.13 m/yr across different regions. The spatial distribution of deformation velocity highlights the regions prone to land subsidence and uplift.

4.2. Time Series Analysis

The time series analysis reveals the episodic events or sudden changes in subsidence and uplift rates. This study also computed the deformation time series from 2016 to 2023 (see Figure 6). The spatial distribution exhibited discernible patterns, illustrating a gradual rise in magnitude and the spread of cumulative deformation over time across the country.
Initially, the time series results typically reveal deformation rates across the study area, offering a reference point for comparison with subsequent measurements. It may correspond with various factors, such as natural geological processes and human activities. Moreover, temporal patterns within the time series data can illuminate seasonal variations in surface deformation rate, driven by various factors such as precipitation and geological formation dynamics.
Subsequently, the progression of surface deformation at specific key locations (identified as D1 to D35 in Figure 5) was examined, and their distinctive cumulative time series subsidence was obtained and analyzed (see Figure 7).
Figure 7 illustrates the observed land subsidence and uplift in the study region. Each point presented in Figure 7 likely exhibits the variation of subsidence or uplift, depicted through color lines to represent the magnitude of vertical displacement. By comparing these deformation patterns across different locations, viewers can distinguish spatial variations and identify areas experiencing more pronounced ground sinking or uplifting. We have noticed eight deformation patterns and plotted the time series of each pattern in Figure 7. The specific deformation patterns are described in Table 2. The causes contributing to the land subsidence and uplift in Rwanda are discussed in Section 5.

5. Discussion and Interpretation

Surface deformation in Rwanda is attributed to various factors, natural and anthropogenic. In Rwanda, the main triggering factors of surface deformation include the tectonic process, rainfall, infrastructure development, and land use/land cover changes [41].

5.1. Volcano, Earthquakes

The distribution and activity of volcanoes in Rwanda are closely linked to the broader geological context of the East African Rift System. This is the tectonic plate boundary that runs through the region. This geological setting, characterized by the divergence of tectonic plates, creates ideal conditions for magma ascent and volcanic eruptions. Some volcanoes, like Mount Nyiragongo and Mount Nyamuragira in the neighboring Democratic Republic of the Congo, exhibit frequent eruptions [42].
There is a strong correlation between volcanoes and surface deformation. Volcanic activity is usually associated with ruptures in the earth’s crust and the activity of plate margins. Faults and fissures are formed when plates squeeze, pull, or rub against each other. These faults and fissures can be conduits for magma to rise, which can lead to volcanic eruptions. When a volcano erupts, magma, gases, ash, and other materials are ejected to the surface, causing surface deformation, such as forming volcanic mounds and craters [43,44]. Secondly, earthquakes also occur as a result of crustal movement. When the stress in the earth’s crust accumulates to a certain level and exceeds the strength of the rocks, the rocks will suddenly fracture, releasing energy and causing an earthquake [45]. Earthquakes are often accompanied by fracturing, uplifting, or sinking of the earth’s surface, causing surface deformation. Also, earthquakes can trigger volcanic activity or cause volcanic earthquakes in volcanic areas [46].
Earthquakes and land subsidence are closely related geological phenomena, often interconnected in several ways. One primary connection lies in the occurrence of tectonic plate movements along faults [47]. Occasionally, earthquakes induce subsidence by activating through secondary geological processes [48]. For instance, seismic shaking can liquefy loose, water-saturated sediments, causing them to compact and settle, leading to subsidence. In Rwanda, the relationship between earthquakes and land subsidence is relatively complex due to the country’s location within the East African Rift System, considered geologically active [49].
The Nyiragongo volcano in the Democratic Republic of the Congo began erupting on Saturday, 22 May 2021, with fissures on the volcano’s southern flank pouring lava into nearby towns [50]. This eruptive event induced a series of seismic events (e.g., Table 3), most of which were shallow earthquakes with a depth of 10 km and a large magnitude, without a main shock and with a small area of influence.
According to [51], the earthquakes with a magnitude larger than four display evident land displacement. Active faults were downloaded on the following website https://earthquake.usgs.gov/earthquakes/search (accessed on 20 October 2023). Figure 8 shows that land deformation in the western part of Rwanda, including the Rubavu and Nyabihu districts, is significantly influenced by volcanic activity and seismic events, given its proximity to the tectonically active East African Rift System. The region’s volcanic soils, formed from past eruptions, are highly susceptible to deformation processes. Volcanic activity, including magma movement beneath the surface, can cause ground swelling and subsidence, altering the landscape. Seismic activity, common in this geologically active zone, further contributes to land deformation.
Table 3 presents the distinct events of the earthquake in Rwanda, mainly in the western part of the country. This area experiences frequent seismic activity, with earthquakes ranging from minor tremors to more significant events that can cause substantial damage and may lead to land deformation.
Figure 9 and Figure 10 indicate that, in 2021, land subsidence increased in the Rubavu and Nyabihu districts, which is associated with the frequency of volcano-tectonic events.
Volcanic eruptions are preceded by ground deformation due to the movement of magma beneath the surface. As magma rises towards the Earth’s crust, it can cause pressure changes and rock fractures, leading to land deformation. This initial accelerated deformation is often an indicator of an impending eruption, as the magma’s buoyancy and the expansion of gases within it force the magma upwards, creating pathways and stressing the crust.
The East African Rift System comprises several fault lines and rift valleys where tectonic forces are causing the Earth’s crust to pull apart slowly (Figure 9). Volcano-tectonic events exert sudden, intense forces on structures, causing them to shift and settle unevenly, resulting in cracks along weak points such as walls, floors, and foundations. According to local people, these cracks happened after the 2021 volcano-tectonic event in the Rubavu district of the Western part of Rwanda (Figure 11). These cracks can range from minor cosmetic issues to severe structural problems, jeopardizing the stability and safety of the building. Additionally, surface deformation, often exacerbated by natural geological processes, can cause the ground beneath buildings to sink or shift, leading to similar cracking and structural instability. This study highlights that the increase in land deformation as time passes in Rwanda is mainly attributed to the volcano-tectonic event in the western region of Rwanda.

5.2. Land Use/Land Cover Change

The Land Use/Land Cover (LULC) classification results indicate a significant alteration in Rwanda in a short span of 13 years (2010–2023), showing an increase in anthropogenic influence over the land surface and a decline in the natural land cover (Figure 12). LULC alteration can significantly contribute to surface deformation [52,53,54]. Land use alterations involve extensive agricultural practices, urban development or infrastructure projects, and change in the forest (Table 4).
This study highlights the increase in land deformation in the Eastern and Northern provinces subjected to changes in LULC over time, such as a decrease in forest and an increase in infrastructure. Figure 7 and Table 2 showed a trend to subsidence, with a maximum cumulative subsidence of more than 20 cm (examples D 24 and D 25).
The observed patterns of land use/land cover change, transitioning from grassland to forest in D24 and from grassland to cropland in D25 between 2010 and 2016, followed by conversion to built-up areas in both regions by 2023, may be indicative of potential drivers of land subsidence. The subsequent transformation of both regions into built-up areas by 2023 likely involved extensive land development, intensifying surface water runoff and altering groundwater recharge dynamics, thereby increasing the risk of land subsidence due to the soil compaction associated with urbanization.
The study conducted by Minderhoud et al. (2018) indicated a strong relationship between LULC change and an acceleration of land subsidence rates [53].

5.3. Rainfall

The surface uplifts to different degrees after the rainfall in the rainy season, directly indicating the linear relationship between rainfall and surface deformation [55]. Rwanda experiences a bimodal rainfall pattern characterized by two distinct rainy seasons. The long rainy season is from March to May, and the short rainy season is from September to November. These seasons alternate with the long dry season. Rainfall is experienced throughout the year in Rwanda, with the most significant occurring from September to May, with an annual precipitation of 1170.2 mm [56]. The country’s topography influences regional variations in rainfall, with the western and northwestern areas, such as the Musanze, Nyabihu, and Rubavu districts, receiving more rainfall than the eastern and southeastern parts, the Kirehe and Ngoma districts. To explore the correlation between rainfall and land deformation in Kigali city, the critical points (D26, D27 and D 28) were used as shown in Figure 13. Rainfall can penetrate the earth and recharge groundwater levels [57]. The interconnection between surface deformation and rainfall is a complex relationship influenced by various geological, hydrological, and anthropogenic factors.
Figure 13 indicates uplift trends, and the results indicate that as rainfall increases so uplift increases. Rainfall may be indicative of potential drivers of land uplifting. In addition, recent study infers that the land uplift rate increases significantly in the months with high average precipitation [58].

6. Conclusions

The main goal of this study is to assess and quantify the extent and magnitude of surface deformation in various regions of Rwanda and analyze the surface deformation over time, using time series data generated by the SBAS-InSAR technique, using ascending-track Sentinel-1 SAR explanations acquired from 2 July 2016 to 8 June 2023 and integrating GACOS to reduce atmospheric distortion of the Sentinel-1 images to assure the accuracy of results. One of the significant benefits of SBAS-InSAR technology is its non-invasive nature, which reduces the need for costly and time-consuming field surveys. The application of SBAS-InSAR technology for surface deformation detection and time series-based analysis holds immense potential in Rwanda as the advanced technology enhances its sustainable development and resilience against geohazards.
The results of this study indicated that the maximum deformation rate was −0.11 m/yr and +0.13 m/yr. The time series analysis results showed a slow increase with time in the eastern and western parts of the country. The current study presented that an earthquake caused land deformation in the western part of the country. Moreover, this study indicates the major contributors to land deformation across the country: Considering the critical points D15, D16, D20, D21, D22, and D23, their deformation was accelerated by volcanic activities and earthquakes. The deformation of D24 and D25 may be attributed to LULC changes over time. Furthermore, D26, D27, and D28 indicated the frequent uplift in Kigali resulted from rainfall variation. Moreover, this research provides crucial information about the latest surface deformation in Rwanda and the neighboring areas. Finally, this study recommends that future researchers investigate more details on the specific natural and anthropogenic factors contributing to surface deformation in Rwanda and the neighboring areas. In the future, the combination of InSAR and other monitoring techniques to explore the links between surface deformation and climate change, as well as the hydrogeological implications, in a region that faces changing precipitation patterns, can enhance the accuracy and comprehensiveness of surface deformation analysis.

Author Contributions

Conceptualization, Z.L. (Zhenhong Li); data collection, A.M.; formal analysis, A.M. and C.S.; investigation, A.M.; methodology, A.M. and C.S.; software, X.Z.; project administration, Z.L. (Zhenhong Li) and C.S.; resources, C.S.; supervision, Z.L. (Zhenhong Li) and C.S.; validation, A.M., C.S., X.Z. (Xuesong Zhang) and B.H.; visualization, writing— original draft, A.M.; writing—review and editing, Z.L. (Zhenhong Li), C.S., B.C., Y.C. and Z.L. (Zhenjiang Liu). All authors have reviewed and consented to the final version of the manuscript for publication.

Funding

This research was funded by the National Key Research and Development Program of China (2021YFC3000400). It was also funded by the Chinese Government through the China Scholarship Council (CSCNo: 2021GBJ004577).

Data Availability Statement

The Sentinel-1 data utilized in this research were obtained from the European Space Agency (ESA) via the Alaska Satellite Facility Distributed Active Archive Centers (ASFDAAC), through this link: https://search.asf.alaska.edu/#/ (accessed on 17 May 2023), earthquake data, to this link: https://earthquake.usgs.gov/earthquakes/search/ (accessed on 20 October 2023) and Rwanda Meteorological Agency (https://www.meteorwanda.gov.rw/index.php?id=2, accessed on 21 October 2023).

Acknowledgments

The authors would like to express their gratitude to the editor and anonymous reviewers for their valuable feedback and recommendations on this manuscript. The authors extend their gratitude to the ESA, for generously providing the data used in this study. In terms of InSAR data processing, the research utilized GAMMA software (version 3.8), and certain graphical components were created using Generic Mapping Tools (GMT).

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. Study area. (a) Map of Rwanda, including four provinces and the capital city, with provincial boundaries indicated by black lines. (b) Map of Africa, country boundaries indicated by blue lines.
Figure 1. Study area. (a) Map of Rwanda, including four provinces and the capital city, with provincial boundaries indicated by black lines. (b) Map of Africa, country boundaries indicated by blue lines.
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Figure 2. Spatial and temporal baselines of interferograms. Blue triangles represent Sentinel-1 SAR image acquisitions.
Figure 2. Spatial and temporal baselines of interferograms. Blue triangles represent Sentinel-1 SAR image acquisitions.
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Figure 3. Workflow of SBAS-InSAR with GACOS.
Figure 3. Workflow of SBAS-InSAR with GACOS.
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Figure 4. Examples of interferograms with GACOS correction. The results before (left) and after correction (right) using GACOS were plotted using the same color and Std is the standard deviation in radian (rad).
Figure 4. Examples of interferograms with GACOS correction. The results before (left) and after correction (right) using GACOS were plotted using the same color and Std is the standard deviation in radian (rad).
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Figure 5. Linear mean velocity in line of sight (LOS) direction. The yellow pentagram represents the selected point, the red line represents the location of the known faults, and the blue and red lines in the rate chart represent uplift and subsidence, respectively. Source of fault data: https://blogs.openquake.org/hazard/global-active-fault-viewer/ (accessed on 20 October 2023). (a–d) correspond to the spatial extent outlined in the left map.
Figure 5. Linear mean velocity in line of sight (LOS) direction. The yellow pentagram represents the selected point, the red line represents the location of the known faults, and the blue and red lines in the rate chart represent uplift and subsidence, respectively. Source of fault data: https://blogs.openquake.org/hazard/global-active-fault-viewer/ (accessed on 20 October 2023). (a–d) correspond to the spatial extent outlined in the left map.
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Figure 6. Cumulative displacement maps from 2016 to 2023 (a–h).
Figure 6. Cumulative displacement maps from 2016 to 2023 (a–h).
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Figure 7. Deformation time series of different critical points. The serial numbers of critical points are labelled above each subplot. (A) Uplift of t he plains area along the southern shore of Lake Edward. (B) Subsidence in the plain’s region near Mbarara. (C) Deformation near Nyiragongo Volcano, (D) Uplift in the capital, Kigali City. (E) Points with subsidence trends. (F) Points with fluctuating deformation. (G) Point D31 with uplift deformation. (H) Point D33 with anomalous deformation time series.
Figure 7. Deformation time series of different critical points. The serial numbers of critical points are labelled above each subplot. (A) Uplift of t he plains area along the southern shore of Lake Edward. (B) Subsidence in the plain’s region near Mbarara. (C) Deformation near Nyiragongo Volcano, (D) Uplift in the capital, Kigali City. (E) Points with subsidence trends. (F) Points with fluctuating deformation. (G) Point D31 with uplift deformation. (H) Point D33 with anomalous deformation time series.
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Figure 8. Land deformation velocity in the western part of Rwanda, including Rubavu and Nyabihu districts (south of the volcano).
Figure 8. Land deformation velocity in the western part of Rwanda, including Rubavu and Nyabihu districts (south of the volcano).
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Figure 9. (a) Epicenters of earthquakes with magnitude of 4.5+ and (b) interferogram of the volcano-tectonic event (20210513-2021052).
Figure 9. (a) Epicenters of earthquakes with magnitude of 4.5+ and (b) interferogram of the volcano-tectonic event (20210513-2021052).
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Figure 10. Details of the time series of points located south and east of Nyiragongo volcano. The green dotted line indicates the 2021 eruption of Nyiragongo.
Figure 10. Details of the time series of points located south and east of Nyiragongo volcano. The green dotted line indicates the 2021 eruption of Nyiragongo.
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Figure 11. Cracks of some buildings in the Rubavu district of Western part of Rwanda due to 2021 volcano-tectonic event. The photos were taken near the key point D20. The red arrows indicate the cracks on the walls. (a–d) show four different examples of wall cracks.
Figure 11. Cracks of some buildings in the Rubavu district of Western part of Rwanda due to 2021 volcano-tectonic event. The photos were taken near the key point D20. The red arrows indicate the cracks on the walls. (a–d) show four different examples of wall cracks.
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Figure 12. Rwanda land use/land cover change.
Figure 12. Rwanda land use/land cover change.
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Figure 13. (a) Relation between annual rainfall and land deformation and (b) velocity map of Kigali city. Data were downloaded from the Rwanda Meteorological Agency (https://www.meteorwanda.gov.rw/index.php?id=2 (accessed on 21 October 2023).
Figure 13. (a) Relation between annual rainfall and land deformation and (b) velocity map of Kigali city. Data were downloaded from the Rwanda Meteorological Agency (https://www.meteorwanda.gov.rw/index.php?id=2 (accessed on 21 October 2023).
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Table 1. Sentinel-1 Data Employed in This Study.
Table 1. Sentinel-1 Data Employed in This Study.
Sensor OrbitPath/FrameCommon Master DateTime Period of Images CollectedNo. ImagesNo. Interferograms
Sentinel 1A/BAscending174/117617 February 20192 July 2016 to 8 June 2023245939
Table 2. Deformation Patterns of Critical Points.
Table 2. Deformation Patterns of Critical Points.
No.PointsTypeDescription
1
Figure 7A		D1, D2, D3, D5, D6Uplift of the plains area along the southern shore of Lake EdwardThese points are located on the southern shore of Lake Edward and show similar deformation characteristics after 2019
2
Figure 7B		D9, D10Subsidence in the plain’s region near MbararaThese points, in the plain’s region near Mbarara, show a broadly subsiding trend, remaining flat between June 2019 and December 2020
3
Figure 7C		D15, D16, D17, D18, D19, D20, D21, D22, D23Deformation near Nyiragongo VolcanoPoints located south and east of the Nyiragongo Volcano show dramatic deformation between March and September 2021, with different deformation trends on either side of the fault. However, point D23, which is far from the volcano, was less affected
4
Figure 7D		D26, D27, D28Uplift in the capital, Kigali CityKigali City shows an upward trend, including the Kigali International Airport area
5
Figure 7E		D4, D8, D13, D24, D25, D30, D35Points of subsidence trendsDuring the monitoring period, these points showed a trend of subsidence, with a maximum cumulative deformation of more than 20 cm
6
Figure 7F		D7, D11, D12, D14, D23, D29, D32, D34Points of fluctuating deformationThe deformation of these points showed a fluctuating trend over the monitoring period, with cumulative deformation in the range of a few centimeters
7
Figure 7G		D31Points of uplift deformationThese points show a significant uplift trend over the monitoring time period, with cumulative deformation exceeding 14 cm
8
Figure 7H		D33Abnormal pointsThese points show an anomalous deformation time series
Table 3. Details of Earthquakes events in western part of Rwanda in 2021.
Table 3. Details of Earthquakes events in western part of Rwanda in 2021.
Time (YYYY/MM/DD)Latitude (°)Longitude (°)Depth (km)Magnitude (M)
22 May 2021−1.750329.2111104.3
23 May 2021−1.965229.6147104.2
23 May 2021−1.812129.3863104.5
23 May 2021−1.82829.438913.064.3
23 May 2021−1.651929.2368104.5
23 May 2021−1.646829.4069104.3
24 May 2021−1.591229.2293104.7
25 May 2021−1.768729.3804104.5
25 May 2021−1.576129.4806104.7
25 May 2021−1.756929.3197104.4
25 May 2021−1.746729.3986104.3
25 May 2021−1.75629.2667104.4
25 May 2021−1.668829.4184104.3
26 May 2021−1.723729.4006104.7
26 May 2021−1.813729.4858104.2
26 May 2021−1.715729.2514104.4
26 May 2021−1.652229.3085104.4
26 May 2021−1.773129.304104.5
26 May 2021−1.739429.255112.654.5
26 May 2021−1.781929.323812.914.5
26 May 2021−1.85529.295712.864.5
27 May 2021−1.616829.3872104.3
27 May 2021−1.714929.3823104.5
Table 4. Annual LULC Change in Rwanda from 2010–2023.
Table 4. Annual LULC Change in Rwanda from 2010–2023.
LULC Class2010 2016 2023
Area (Km2)Rate (%)Area (Km2)Rate (%)Area (Km2)Rate (%)
Open water1518.98855.871569.49556.071603.25756.24
Forest6643.35625.674103.037515.864584.360617.85
Aquatic Vegetation971.7033.76970.8233.75956.673.72
Crop land11,068.9242.784783.190718.486899.541526.86
Built-up area207.48870.802103.33868.133397.115313.23
Bare land23.23980.097.38660.038.38010.03
Glass land5441.588721.0312,339.038247.688235.045132.06
No data1.0430.000.01760.000.00220.00
Total 25,876.3277100.0025,876.3277100.0025,684.3723100.00
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Mugabushaka, A.; Li, Z.; Zhang, X.; Song, C.; Han, B.; Chen, B.; Liu, Z.; Chen, Y. Mapping Surface Deformation in Rwanda and Neighboring Areas Using SBAS-InSAR. Remote Sens. 2024, 16, 4456. https://doi.org/10.3390/rs16234456

AMA Style

Mugabushaka A, Li Z, Zhang X, Song C, Han B, Chen B, Liu Z, Chen Y. Mapping Surface Deformation in Rwanda and Neighboring Areas Using SBAS-InSAR. Remote Sensing. 2024; 16(23):4456. https://doi.org/10.3390/rs16234456

Chicago/Turabian Style

Mugabushaka, Adrien, Zhenhong Li, Xuesong Zhang, Chuang Song, Bingquan Han, Bo Chen, Zhenjiang Liu, and Yi Chen. 2024. "Mapping Surface Deformation in Rwanda and Neighboring Areas Using SBAS-InSAR" Remote Sensing 16, no. 23: 4456. https://doi.org/10.3390/rs16234456

APA Style

Mugabushaka, A., Li, Z., Zhang, X., Song, C., Han, B., Chen, B., Liu, Z., & Chen, Y. (2024). Mapping Surface Deformation in Rwanda and Neighboring Areas Using SBAS-InSAR. Remote Sensing, 16(23), 4456. https://doi.org/10.3390/rs16234456

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