Analysis of shoreline dynamics of Lake Sibaya in South Africa between 1986 and 2020 using the Landsat series and DSAS algorithm

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Analysis of shoreline dynamics of Lake Sibaya in South Africa between 1986 and 2020 using the Landsat series and DSAS algorithm

https://doi.org/10.21203/rs.3.rs-4956949/v1

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Lake Sibaya is the largest nearshore freshwater lake in South Africa. It is ecologically and socioeconomically linked to the human development agenda in the northeastern coastal region of the country. It is part of the iSimangaliso Wetland Park, the first proclaimed World Heritage Site in the country and the only water resource serving surrounding areas. However, Lake Sibaya is reportedly drying up rapidly for various reasons (e.g., commercial forestry), yet the extent and pattern of this drying are unidentified. Here, we used the Landsat satellite data series and the Digital Shoreline Analysis System (DSAS) algorithm to analyze the changes in the shoreline of Lake Sibaya between 1986 and 2020. The study results indicate that Lake Sibaya displayed major retracting changes from 70.6 km2 in 1986 to 49.5 km2 in 2020, with marked erosional changes in the regions of the southeastern basin and northern arm. This occurred with a net shoreline displacement of − 1338 m and an end point rate of − 39 m/year, averaging ± 6 m/year. Our results projected further lake retreat from 49.5 km2 in 2020 to 39.1 km2 in 2030. We observed vegetation growth on sandbanks separating the southeastern basin from the main system, threatening to isolate these systems entirely. Improved rainfall is expected to reconnect these basins because they rely heavily on groundwater recharge. Otherwise, activities served by the southeastern basin will be affected. We recommend further research to accurately quantify the levels of abstraction by different users, including fine-scale mapping of planted forests surrounding this lake.

Geographic Information Systems

Physical Geography

Hydrology

Lake Sibaya

DSAS

Landsat

Shoreline analysis

Lake Sibaya is the largest nearshore freshwater lake in South Africa; it was formerly an estuary that was completely separated from the sea by the gradual formation of forested coastal dunes (Allanson 1979) on the northeast coast of KwaZulu-Natal Province. The lake is part of the iSimangaliso Wetland Park (Obura 2012), the country's first proclaimed World Heritage Site, with various wetlands comprising its biodiversity. Therefore, like many inland waters, Lake Sibaya provides a variety of ecosystem goods and services and has essential functions in the natural environment. It is an attractive tourist destination, and its hydrological features are impacted by developments in its catchment area (Nsubuga et al. 2019). The lake is an essential source of fresh water for domestic water supply, performs ecological functions, and is often the only water source for certain animals during drought (Weitz and Demlie 2014). In addition to Lake Sibaya’s global and national significance (CEPF 2010) is that it is crucial for major scientific study, with six locations across the nation chosen by the Expanded Freshwater and Terrestrial Environmental Observation Network (EFTEON) for host ecosystem research projects (Feig 2018).

There is growing concern about the deterioration of Lake Sibaya, and the recent publication titled "The Slow Death of Lake Sibaya" by Carnie (2020) has reinforced this impression. The lake is increasingly exposed to the negative impacts of increased rural and forestry development (Everson et al. 2019) and increasingly dry climate conditions (Nsubuga et al. 2019). Smithers et al. (2017) reported an approximately 35% decrease in the water level of Lake Sibaya since 2001, which can be attributed to the impact of commercial forestry plantations. However, some uncertainty regarding the exact history, magnitude, and effects of these plantations on the catchment was noted. Smithers also stated that the ten years of significantly lower-than-average rainfall from 2001 to 2011 was believed to be the primary cause of the decrease in Lake Sibaya's level since 2001. These dynamics add significant complexity to managing lakes and other inland water systems. Over the past two decades, lake water levels have decreased significantly, exposing large areas that were once flooded (Mpungose et al. 2022). While surrounding systems such as Lake St. Lucia and local dams have gradually improved since the drought, Lake Sibaya has not and continues to deteriorate (Carnie 2020)—a severe cause for concern! The results of the present study are unique because they shed light on the changes in the shoreline of Lake Sibaya.

As part of the sustainable management of water resources, regularly monitoring lakes and marking shoreline changes are essential (Dereli and Tercan 2020). This approach is necessary in much of Africa, where the difficulty of monitoring lakes poses a challenge even with best-funded monitoring programs (Ballatore et al. 2014). This task is even more critical in a water-scarce country such as South Africa, where water resources must be managed sustainably (Snaddon et al. 1998). The commonly used procedure for assessing the dynamics of coastal areas and inland waterbodies is shoreline change analysis, a sensitive indicator of the vulnerability of these systems to naturally and human-induced forces (Genz et al. 2007).

A shoreline is an interface between a waterbody and land, the movement of which is a complex process associated with changes in precipitation, evaporation, runoff, and human land-use activities and their interactions (Temiz and Durduran 2016). Therefore, shoreline change detection is crucial for providing a spatiotemporal analysis of freshwater resources (Duru 2017) to aid in formulating protection strategies. Therefore, many tools are utilized for shoreline change analysis. Prior shoreline change evaluations were straightforward and based on direct comparisons with preexisting maps (Burningham and Fernandez-Nunez 2020). However, estimates of precision and uncertainty were only possible during this time. Thus, the 1970s saw a complete transformation of this shoreline change study technique due to advances in computer technology and the associated Geographic Information System (GIS). Hitherto, concerted research efforts have been reflected in the growing number of shoreline change studies, most of which use remote sensing (Ankrah et al. 2022) to understand the dynamics of inland waters against various natural and anthropogenic pressures.

Remote sensing, particularly Landsat products, are indispensable data sources for shoreline extraction due to their longevity, free access, and good coverage with a 30-m ground sampling distance. Several studies have exploited Landsat data to characterize inland waterbodies (Hovsepyan et al. 2019; Yue et al. 2021). The basis for determining the shoreline using remote sensing is the spectral difference between the waterbody and the surrounding land area (McFeeters 1996), and its accuracy depends on the sensor's native resolution and how the position is mapped (Smith et al. 2021). Several studies have effectively examined shoreline changes in many lakes using the modified normalized difference water index (MNDWI; Xu 2006) based on Landsat data. For example, Tao et al. (2015) used multiyear Landsat imagery from 1987 to 2010 on the Mongolian Plateau and reported rapid losses in these lakes resulting from variations in precipitation, mining and agricultural practices. So, for the preservation and sustainability of wetlands, estimating shoreline change rates utilizing RS technology has emerged as a crucial area of study (Bouchahma and Yan 2014; Bacino et al. 2020; Valderrama-Landeros et al. 2020). Therefore, to measure shoreline changes using RS images, several methods are available, including a cell-to-cell comparison of binary images and the Digital Shoreline Analysis System (DSAS) extension in ArcGIS software (Bouchahma and Yan 2012; Rezaee et al. 2019).

In this article, we aim to analyze the historical shoreline changes in Lake Sibaya between 1986 and 2020 with the Landsat series and DSAS geospatial tool to identify vulnerability based on two statistical variables, namely, Net Shoreline Movement (NSM) and the End Point Rate (EPR). Additionally, the DSAS was utilized to predict future shoreline changes for the next ten years (2030). Our novel contribution to this study is to determine the extent of shoreline displacement and identify vulnerable edges of Lake Sibaya, as there is still no research tracking the evolution of these shores.

2.1. Study Area

We conducted this study at Lake Sibaya on the northeast coast of South Africa, approximately 170 km north of Richards Bay and 60 km south of the border with Mozambique (Fig. 1). The lake is the most extensive natural coastal freshwater system in the country and covers an area of nearly 70 km² (Combrink et al. 2011) at 20 m above sea level (Stager et al. 2013). Lake Sibaya was once an estuary before it was separated from the Indian Ocean by the gradual formation of a coastal forest dune (Allanson 1979). It has an average depth of 13 m with a maximum of 43 m (Bowen 1978). It is home to a wide variety of plants and animals, including the second-largest population of Nile crocodiles and hippopotamus in the KwaZulu-Natal Province (Ward and Kyle 1990). No rivers flow in or out of Lake Sibaya. This area has a subtropical climate with warm summers characterized by average daily temperatures ranging from 21.0°C to 32.1°C and mild winters ranging from 10.9°C to 26.4°C (SAWS 2018). It receives summer precipitation, most of which falls between October and March, with pronounced spatial and temporal variations (Ramjeawon et al. 2020).

2.2. Landsat Data and Shoreline Extraction

In this study, we used data collected by the Landsat constellation of satellite products obtained free of charge from the United States Geological Survey; these data are available at http://earthexplorer.usgs.gov (accessed August 03, 2023). The characteristics of the Landsat images are summarized in Table 1. Seasonality from annual composite.

Table 1

Specifications of the Landsat data used in the study.
Satellite	Sensor	Image date
Landsat-5	TM	14 November 1986
		02 June 1990
		12 March 1995
		25 March 2000
		24 April 2005
		20 December 2009
Landsat-8	OLI/TIRS	23 June 2015
Landsat-8	OLI/TIRS	20 June 2020

The shoreline detection method using remote sensing relies on the spectral differences between water and land in the visible and shortwave infrared channels of the electromagnetic spectrum. Here, we classified Landsat imagery for each year based on the modified normalized difference water index (MNDWI; Xu 2006). The MNDWI is calculated as the difference between the shortwave infrared (SWIR) and green (Green) bands of the electromagnetic spectrum over their sum (Eq. (1)).

$$\:\text{M}\text{N}\text{D}\text{W}\text{I}=\frac{\text{S}\text{W}\text{I}\text{R}-\text{G}\text{r}\text{e}\text{e}\text{n}}{\text{S}\text{W}\text{I}\text{R}+\text{G}\text{r}\text{e}\text{e}\text{n}}$$

To extract the waterbody while maintaining shoreline accuracy, we implemented a raster-to-vector conversion in ArcMap, after which the polygon geometry of Lake Sibaya was saved for each date. We then overlaid each polygon with corresponding Landsat images and visually inspected the boundary on-screen to ensure the accuracy of the shoreline extraction results. We extended this exercise with false-color Landsat images, following (Verpoorter et al. 2012) observation that most lake boundaries match well with false-color images. Furthermore, smaller patches of vectors surrounding the lake border were manually erased to create a clean geometry.

2.3. Shoreline Change Analysis

We used the Digital Shoreline Analysis System (DSAS) V5.0 extension developed by the United States Geological Survey (USGS) within the ESRI ArcGIS (ArcMap 10.6) environment to analyze shoreline changes (Himmelstoss et al. 2018). DSAS provides a unique opportunity to compute the rate-of-change statistics necessary to understand shoreline dynamics and to forecast a decade or two of shoreline possibilities, and its importance as a scientific tool to quantify spatial and temporal variations in shoreline changes has been described previously Mondal et al. (2020). We imported multiple historical shoreline positions of Lake Sibaya for the years 1986, 1990, 1995, 2000, 2009, 2015, and 2020 into the DSAS system. In the DSAS, transects were set to cast at a maximum search distance of 1800 m from the baseline, transect spacing of 200 m, smoothing distance of 1000 m, and default uncertainty of 10 m. The shoreline threshold was set at 6 for statistical values, and the confidence level was set at 90%.

The horizontal changes in the shoreline and rates of change were subsequently established. These metrics included (a) the Net Shoreline Movement (NSM), which indicates the distance between the oldest and newest shorelines; and (b) End Point Rate (EPR), which is the distance of shoreline movement between the oldest and the newest shoreline positions divided by the time lapse and is one of the most widely used methods in shoreline change research studies carried out globally (Himmelstoss et al. 2018).

2.4. Future shoreline

The EPR model is often the foundation of methods used to forecast future shorelines (Esmail et al. 2019). According to (Nandi et al. 2016), many recent studies have confirmed the EPR as the most effective statistical method for forecasting future temporal changes in shoreline dynamics. A statistically determined collection of Linear Regression Rate (LRR) values is used by the Kalman filter to more precisely anticipate the location of the future shoreline (Yan et al. 2021). The subsequent shoreline positions are approximated with time by combining historical shorelines. Then, by updating the rates and uncertainties, the Kalman filter reduces the difference between the modeled shorelines and those observed (Long and Plant 2012). With these updated data, the shoreline position is forecasted for each subsequent period. The predicted shoreline is displayed along with an uncertainty band since the shoreline uncertainty reflects the measurement inaccuracy (Himmelstoss et al. 2018). Even though the shoreline is a very dynamic feature, the forecast aids in planning preventative actions to conserve the shoreline of vulnerable regions (accretion/erosion).

Based on the calculated statistical data, the DSAS beta model was able to forecast the shoreline for the year 2030. It is crucial to remember that not all data types, localities, and shoreline change patterns are appropriate for this forecasting tool. When determining whether it is wise to utilize this tool to project an anticipated shoreline position, users must consider the parameters and constraints of their data (Himmelstoss et al. 2018). The shoreline change analysis and future projection are summarized in (Fig. 2).

3.1. Spatio-temporal changes in Lake Sibaya over the 1986–2020 period

We present the spatiotemporal changes in Sibaya Lake in Fig. 3(a), which shows the surface cover (polygons), and in Fig. 3(b), which shows the lake boundaries (polylines) for each recorded year. Our results in Figs. 3(a) and (b) show a considerable receding trend, particularly at the base of the northern arm and in the southeastern and southwestern basins. Over time, the cordon-like system is progressively closing between the main and southeastern basins and became more apparent after 2015, resulting in the separation of these basins. The ominous appearance of plant (i.e., Juncus oxycarpus) patches and the expansion of sand between these basins point to their complete separation. Relatively small but noticeable changes occur over the western arm of the lake.

In Fig. 4, we show the surface area changes over the lake in each sampled year. It had the largest area in the earlier years of the study period, with 70.7 km² in 1986, and increased to 71.1 km² in 1990. In 1995, the area decreased to 66.7 km² and recovered in 2000. From the mid-2000s, the lake exhibited a steady downward trend from 61.9 km² to its lowest level (49.5 km²) in 2020. This implies that Lake Sibaya suffered a decrease of 32.5% between 1986 and 2020. As expected, the water level followed the same pattern, dropping from 19.4 m to 14.8 m over the study period (Fig. 4).

3.2. Shoreline change analysis between 1986 and 2020

Our analysis is based on shoreline movements in 465 transects along Lake Sibaya between 1986 and 2020, encapsulated in Table 2 and Figs. 5 and 6. As shown in Fig. 5, the lake shorelines exhibited high variability between the recorded years and generally displayed a receding pattern. The land-to-lake shift varied between − 10 m and − 1338 m over the study period, with the most extended shoreline displacement (NSM) reaching − 1338 m in the southwestern basin, followed by − 1119 m at the base of the northern arm.

Table 2

Statistical quantification of shoreline movement.
No. of Transects	Metric	Max	Min	Avg.	Net erosion (%)	Max erosion
465	NSM (m)	−1338	-	−201	99.6	-
465	EPR (m/yr)	0	-	−6	99.6	−39

Figure 6 represents the EPR, which shows a receding pattern between the southeastern and southwestern regions, including the northeastern region of the northern arm. Statistically, the EPR is − 39 m/yr, with an average of − 6 m/yr. Both methods also yielded similar results regarding the retreat patterns along the shoreline over the study period. Little change is observed in the western arm and east of the main basin.

3.3 Prediction of shoreline change in Lake Sibaya for 10 Years

Lake shoreline forecasting is essential for informing lake authorities and other interested stakeholders about future changes. Based on the 1986–2020 shoreline statistics, we used 2020 to forecast the shoreline for 2030. This was done to indicate where the shoreline could be in the next decade and where the most vulnerable edges of concern need immediate attention. In Fig. 7, the black line indicates 2020, and the red line indicates 2030. Our prediction analysis points to a further decline in the lake surface area from 49.5 km² in 2020 to 39.1 km² in 2030. A large amount of change is notable at the base of the northern arm and in the region between the southeastern and southwestern basins. As previously mentioned, the separation of the southeastern basin from the main system is complex and represents an exciting test of the DSAS tool for lakes with breaching shorelines.

The study of shoreline dynamics at Lake Sibaya between 1986 and 2020 using Landsat series and DSAS analyses has allowed the characterization of changes over 34 years. Our results showed that Lake Sibaya experienced the most remarkable shoreline changes at the base of the northern arm and between the southeastern and southwestern basins, with a maximum NSM of − 1 338 m at an average of − 201 m and an average EPR of − 6 m/yr. The lake area decreased from 70.6 km² to 49.5 km² during this period, corresponding to a loss of 32.5%. These results show a similar declining pattern (83.7 km² in 1992 to 58.8 km² in 2016) as Nsubuga et al. (2019), although their estimates were slightly greater than those reported here. This receding modification of the system promoted the development of sandbars that threaten to separate the southeastern basin from the main system (Fig. 8). This occurrence resembles the development of the dune cordon that separates the Lake Sibaya estuary from the Indian Ocean (Whyte 1999). Our results corroborate previous work (Carnie 2020, Ramjeawon et al. 2020) that the separation between these basins transpired in 2015 after the lowest recorded lake level in history.

Notably, Lake Sibaya is the only water resource that serves multiple users in the region. With the rifting of the southeastern basin that dropped below the main system, it still supplies Mbazwana and Sodwana Bay (Carnie, 2020). A further decrease in these parameters could have severe implications for these areas. In addition to the water needs of the surrounding area, lakes also play a crucial role in maintaining ecological balance and agricultural activities (Cao et al. 2023; Sun et al. 2023). For example, littoral species such as microinvertebrates, algae, and microorganisms may have specific habitat requirements, which may be affected by shoreline displacements and result in the loss of some intolerant species (Duru, 2017). While uncertainties remain surrounding this "new normal", research is underway to understand the implications of this development. Our prediction analysis also confirmed further retraction to 39.1 km2 in 2030 and the complete separation of these systems. Several studies have attributed this decline to the interaction of drier climatic conditions and forestry expansion, which has led to conflicts over water resources between people and livestock and economic activities such as issuing commercial forestry licenses (SAEON 2023).

The threatening changes to Lake Sibaya are not uncommon, as many lakes worldwide are experiencing increasing human- and naturally induced disturbances (Han et al. 2024), most of which have been captured through Landsat and the DSAS geospatial tools. For example, Yue et al. (2021) reported a shoreline retreat of 1397 m in Hulun Lake between 1982 and 2012 in China. KOÇ and BAŞ (2024) studied Lake Burdur in Turkey and found that it retreated by 5401 m during 2013–2023. AghaKouchak et al. (2015) quantified the surface area dynamics at Lake Urmia in northwestern Iran and confirmed a decline of 88% during the period 1972–2014. Taken together, the shoreline variability analysis provides insights into the lake water levels, primarily reflecting the changes in precipitation and human activities within the lake (Temiz and Durduran, 2016). Similarly, in this study, Landsat and DSAS data allowed us to quantify and understand the changes in Lake Sibaya between 1986 and 2020 and the expected changes by 2030.

In this study, we focused solely on analyzing shoreline changes. However, it is important to note that the factors influencing its variability are outside the scope of this article. Based on our findings, the following insights may be valuable: First, examining high-density time series with available satellite data could reveal interannual shoreline variability. Second, detailed quantification of water users in and around the lake is crucial for formulating future planning and conservation strategies. This includes mapping unauthorized planted forests at a fine scale. Third, studying vegetation development on banks and understanding morphological changes are essential. 4. Finally, studying groundwater variability is necessary to understand why Lake Sibaya is not recovering while smaller water bodies in this region are recovering.

This study presents an analysis of shoreline changes in Lake Sibaya from 1986 to 2020 using Landsat series data and the DSAS tool, with predictions for the state in 2030. Our results revealed a robust retreating pattern of the shoreline, especially in the northeastern arm and between the southeastern and southwestern basins. The most significant shoreline displacement (NSM) is − 1 338 m, and the most significant shoreline displacement (EPR) is − 6 m/yr between the southwestern and southeastern basins. This retreating shoreline movement led to the separation of the southeastern basin from the main system in 2015. A range of landscape scenarios with clear implications for systems dependent on the southeastern basin were highlighted. These systems are expected to be reconnected temporarily during good rains, and the vegetation in the sandbars separating these systems is developing rapidly. However, further research is needed to understand these implications and other underlying factors involved in this retreat. Finally, the integration of Landsat and DSAS data provides a unique opportunity to understand the shoreline variability of Lake Sibaya. Continued advances in sensor technology are expected to add additional value in the future.

Acknowledgments The author is immensely grateful to Sue Janse van Rensburg of the South African Environmental Observation Network (SAEON) for their support in sharing the required data.We would also like to thank Sifiso Khathi of Fugro GB (North) Marine Limited, United Kingdom, and Sipiwe Ngwenya of the University of the Free-State, South Africa, for all the technical assistance needed.

Author contributions FKK: Conceptualization, methodology, validation, investigating, software, formal analysis, resources, data curation, Writing – original draft, visualization. SX: Conceptualization, Writing – review & editing, funding acquisition, visualization, investigating, formal analysis, supervision. MG: Writing – review & editing, funding acquisition, visualization, investigating, formal analysis, and supervision.

Funding This research was supported by Council for Geoscience (Grant no: N/A).

Data availability Data will be made available upon request.

Ethical Approval This research does not contain any studies involving human participants or animals performed by any of the authors.

Consent to Participate The authors agree to participate in the journal.

Consent to Publish The authors agree to publish this article in the journal.

Competing interest The authors declare that they have no conflicts of interest.

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The authors declare no competing interests.

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Analysis of shoreline dynamics of Lake Sibaya in South Africa between 1986 and 2020 using the Landsat series and DSAS algorithm

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Abstract

Figures

1. Introduction

2. Materials and Methods

2.1. Study Area

2.2. Landsat Data and Shoreline Extraction

2.3. Shoreline Change Analysis

2.4. Future shoreline

3. Results

3.1. Spatio-temporal changes in Lake Sibaya over the 1986–2020 period

3.2. Shoreline change analysis between 1986 and 2020

3.3 Prediction of shoreline change in Lake Sibaya for 10 Years

4. Discussion

5. Limitations and prospects

6. Conclusion

Declarations

References

Additional Declarations

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