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The use of Digita Shoreline Analysis System (DSAS) to assess Parnaíba
Delta (NE, Brazil) short-term morphodynamics conditions.
Thiago Augusto Bezerra Ferreira¹. André Giskard Aquino da Silva¹. Yoe Alain Reyes Perez¹. Helenice Vital¹, Karl Stattegger2,3
1Programa de Pós-Graduação em Geodinâmica e Geofísica. Universidade Federal do Rio Grande do Norte, Brazil 2Adam Mickiewicz University, Poznań, Poland
3Kiel University – Germany
E-mail addresses: ferreira.augustus@gmail.com (T.A.B. Ferreira); andregiskard@hotmail.com (A.G. Aquino da Silva);
yoealain@yahoo.com (Y.A.R. Perez); helenice@geologia.ufnr.br (H. Vital),
Abstract
Deltaic regions are transitional environments, where continuous changes at different temporal and spatial scales occur, which are related to several processes such as oceanographic, fluvial, climatic and anthropogenic. Continuous monitoring of this environment provide information for understand the spatial distribution of erosive/depositional scenarios, hence, its development. The main objective of this research was to investigated the coastline behavior of the Parnaiba River Delta (PRD) between 1984 and 2017, determining the rates of retreat and progradation of shorelines using the satellite images and statistical methods. The PRD coastline is approximately 100 km long, therefore it was divided into four sectors (I – IV). From the Weighted Linear Regression (WRL) values, which is a statistical method used by the Digital Shoreline Analysis System (DSAS), it was possible to classify the delta coastline into five classes, regarding shoreline changes: intense retreat, moderate retreat, stable area, moderate progradation and intense progradation. The results revealed that 21% of the beaches on the delta are under intense shoreline retreat, 30% under moderate shoreline retreat, 4% are stable, 29% under moderate shoreline progradation and 15% under intense shoreline progradation. Considering the mean (-0.44 m / year) and median values (-0.49 m/year), the entire delta can be classified as a relatively stable region. Differently from most deltaic coastlines worldwide, the PRD is not severely impacted by anthropogenic action, suggesting it snaturally developing condition to be the factor responsible for the short-term stability of the PRD shoreline.
Introduction
River deltas worldwide are densely populated areas where large cities have been developed (Beatley et al. 2002; Anthony 2015). Increasing urbanization in the last decades lead to several changes on the delta area (constructions of coastal infrastructure, land reclamation, river dams, sand mining, deviation of river course, ground water extraction) causing acceleration of erosive processes on the coastal zone, resulting in socioeconomics and environmental damages (Bird 2008; Anthony et al. 2015; Ghoneim et al. 2015; Corbau et al. 2019). Therefore, understanding the spatial distribution of erosion risks on the deltaic coastal zone is of extreme importance to the development of mitigation plans (Beatley et al., 2002; Quiao et al., 2018). In this frame, continuous monitoring of coastal environment provide data to understand its morphodynamics, helping predicting the development of erosional/depositional trends (Luijendijk et al. 2018; Qiao et al. 2018; Vousdoukas et al. 2018).
In order of monitoring coastal environments, the understanding of the changes in shoreline position is fundamental. The shoreline, is defined as the interface between land and water, and it is considered a geomorphic system undergoing continuous changes, which occur in several spatial and temporal scales (Beatley et al. 2002). The use of orbital remote sensing (Landsat, Sentinel, Spot, Ikonos) is considered one of the best methods for monitoring shorelines changes. This technique allows acquiring consistent data from large geographic areas at regular time intervals, hence, it is more efficient than conventional techniques (Gens 2010; Ghoneim et al. 2015; Zhai et al. 2015; Luijendijk et al. 2018; Qiao et al. 2018).
Recently, several studies using orbital remote sensing have been done to quantify shoreline changes in deltaic environments such as the Nile delta (Ghoneim et al. 2015), Ebro and Rhone (Besset et al. 2017), Niger (Dada et al. 2018), Po (Bittencourt et al. 2007) and Mekong (Anthony et al. 2015). These studies have demonstrated deltaic coastal erosion as direct consequence of anthropogenic impacts on the natural system. On the other hand, the Parnaiba’s River Delta (PRD), located in northeast Brazil, has only little human interventions on the delta area, as well as the catchment area of the river. In this context, the
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short-term (decadal) evolution of this delta coastline increases the understanding on the behavior of a naturally developing system under the pressure of sea-level rise. Therefore, the main objective of this work is to use satellite imagens for mapping the temporal evolution of the PRD coastline, from 1984 to 2017, and discuss the main causes of its variation.
Study Area
The PRD is located on northeast Brazil, between the states of Maranhão and Piauí (Fig.1), covering approximately 3138 km2 (MMA,2006) where more than 280,000 inhabitants living on (IBGE, 2016).
These states are located between pre-Amazon wet and NE semi-arid climatic zones (Köppen 1936). This transition region exhibits a rainy season from January to May, with a monthly rainfall average over 150 mm, followed by a dry season from June to December (Paula et al, 2016; 2018), and the coast is subject to Equatorial Atlantic Air Mass with strong northeast trade winds blowing most of the year with an average of 4.4 m/s (Paula et al, 2016; 2018).
Figure 1: Location of study area in relation to South America (a), Brazil (b) and northeast brazil (c). Description of the major features on the delta and the sectors I to IV, in which the delta coastline was divided (c) .
The PRD is an asymmetric delta, dominated by both waves and tides, with a shallow and narrow continental shelf offshore (Szczygielski et al. 2014; Aquino da Silva et al. 2015b). Tidal amplitudes of the meso-tidal regime vary between 1.70 and 3.06m for neap and spring tide conditions respectively (Aquino da Silva et al. 2015b). The wave climate is dominantly N-NE and E-NE-directed with a significant wave height between 0.60 to 1.1 meters, and an average period of 6 to 9 s (Paula et al, 2016). Winds and wave climate generate a longshore current with predominant east-west direction.
The Parnaiba’s River (PR) is the second largest river of NE Brazil, in terms of length (1400 km long) and drainage basin size (344.122 km²) (MMA, 2006). The mean annual water discharge is 841 m³/s, measured at Luzilândia station (approximately 120 km from its mouth, which is closest to the study area), with suspend sediment concentration (SSC) over 50 mg/l and an annual suspended sediment load (SSL) around 2.54x106 ton (MMA, 2006). In its midstream (approximately 700 km from its mouth), there is a
river dam at Parnaíba River (PR) midstream, where operates Boa Esperança hydroelectrical power station.
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from the Silurian until the Cretaceous (Góes and Feijó 1994). The Barreirinhas basin has similar sedimentary composition, and its formation is related to the fragmentation of the supercontinent Gondwana, during the Mesozoic (Feijó 1994). Rock types of both basins range from conglomerates and coarse-grained sandstones to shales and siltstone. Both basins have strong structural component with occurrence of listric normal faults and strike-slip faults (Feijó 1994; Góes and Feijó 1994). Turbidity data collected before and after the river dam demonstrates that most of the sediment is generated downstream from the dam (Aquino da Silva et al., 2019).
PRD can be divided in two parts, eastern and western, where two types of coasts dominate (Szczygielski et al. 2014). The eastern part (sectors I e II, Fig. 1) consists of an actively migrating coastal dune field, beaches (up to 200 m wide) and prograding barriers islands. In the western part, tidal channels associated with estuarine-lagoonal conditions, narrow beaches, several sand spits dominate the coast. In 1996 the PRD, as well as its adjacencies, became an Environmentally Protected Area in order to preserve its biodiversity and landscape.
Materials and methods
Images from LANDSAT series satellites were used to map the changes on PRD coastline between 1984 and 2017. The proposed methodology (Fig. 2) consist of three main steps: (a) digital image processing to highlight the land/water boundary, (b) automatic shoreline extraction and bias analysis and (c) historical analysis of the shoreline through Digital Shoreline Analysis System (DSAS).
Figure 2: Conceptual flow chart in this study: (a) Acquisition and digital image processing, (b) automaticshoreline’s extraction and (c) historical analysis of the shoreline through DSAS.
Digital Image Processing of Landsat Satellite images
The shoreline positions of the PRD (table 1) were extracted from Landsat satellite images (sensors TM, ETM+ and OLI/TIRS) at an unequal interval between 1984 to 2017. The images ware a Level 1 Terrain corrected (L1T) product, which are radiometrically and geometrically terrain corrected.
Table 1: Information about Landsat images and uncertainty analyzes. Notes: PE – Pixel error, RE – Rectification error
and TE – Total Error.
Image Data (yyyy/mm/dd)
Sensor Type Image time acquisition (GMT) Tidal height at imagery time Time of highest tide recorded on day of image Highest tide recorded on day of PE (m) RE (m) TE (m)
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The geometric registration process was applied to improve the position of the satellite images, using the Landsat-8 OLI image from 2017 as baseline to perform image-to-image registration. More than 20 commonly ground control points (GCP) were identified, such as intersections of highways/avenues, being used to resample from the first order polynomial transformation. After this procedure the Root Mean Square Error (RMSE) was less than 8.0 m (Table 1).
The atmosphere affects the radiance and reflectance received at the satellite by scattering, absorbing, and refracting the electromagnetic wave (Chávez 1996). Therefore, in order compare data obtained under different atmospheric conditions such interference must be corrected. A widely accepted method to compensate the atmospheric these effects caused by haze, dust or smoke, is the dark object subtraction method (DOS) (Chavez, 1996). This method is simple, robust and accurate for atmospheric correction, especially when dealing with low reflectance as it is common in aquatic environments (Aquino da Silva et al., 2015)
Automatic shoreline extraction
The "Modified Normalized Difference Water Index" - MNDWI (Eq.1). This index was created because the water pixels have a maximum reflectance in the green band and a higher absorption (minimum reflectance) in the MIR band. In turn, soil, vegetation and dry sand have higher reflectances in the MIR band. As result of MNDWI, a new raster image with values ranging from +1 to -1 is generated. The water pixels are enhanced, presenting positive values, while vegetation pixels, dry sediments and soil are suppressed, exhibiting negative values (Xu 2006). Some authors defined the optimal threshold to segregate water and land as zero (Xu, 2006; Fisher et al., 2016; Kelly and Gontz, 2018). Finally, each binary image was converted into polygons and later into polylines.
𝑀𝑁𝐷𝑊𝐼 𝐼𝑛𝑑𝑒𝑥 =𝐺𝑟𝑒𝑒𝑛 − 𝑀𝐼𝑅
𝐺𝑟𝑒𝑒𝑛 + 𝑀𝐼𝑅 (𝐸𝑞. 1)
Bias analysis
The variations of shoreline position occur at different spatial and time scales. On short time scales (hours to years) such variations are directly related to wave action, storms surges, tides, currents, among others (Beatley et al. 2002). Therefore, it is necessary for the interpreter to identify and quantify the uncertainties related to the variation of shoreline’s position, including them in the model to assure its reliability (Romine et al. 2009; Del Río and Gracia 2013). Uncertainty analysis pointed out four possible sources of error, which were: rectification error (RE), Pixel error (PE), seasonal error (SE) and tidal error
(TDE).
The rectification error (RE) is expressed by the georeferencing RMSE of the satellite images (or
aerial photographs). The pixel error (PE) is equal to the satellite spatial resolution, as demonstrated in table
1, decreasing with the increase of sensors spatial resolution. The seasonal error (SE) is associated with the
interannual depositional and erosional cycles occurring on the beach profile, which naturally changes its inclination. Tidal error (TDE) is related to the daily displacement of the shoreline position due to the tidal
amplitude. The magnitude of TDE depends of the inclination of the beach profile in combination with the
(m) acquisition (GMT) image acquisition 2017-09-28 Landsat 8 OLI 12:58 2.37 13:17 2.37 30 ---- 30 2014-08-03 Landsat 8 OLI/ 12:58 2.64 12:48 2.65 30 4.2 30.29 2012-10-08 Landsat 7 ETM+ 12:54 2.35 13:12 2.25 30 7.1 30.83 2009-10-24 Landsat 7 ETM+ 12:48 2.50 13:02 2.55 30 6.21 30.63 2007-10-03 Landsat 5 TM 12:51 2.38 13:15 2.38 30 5.10 30.43 2004-08-07 Landsat 5 TM 12:41 2.61 12:35 2.61 30 5.89 30.57 2001-12-21 Landsat 5 TM 12:38 2.46 13:08 2.49 30 6.30 30.65 1999-11-14 Landsat 5 TM 12:51 2.54 13:19 2.56 30 4.70 30.37 1995-11-03 Landsat 5 TM 11:58 2.62 12:45 2.72 30 7.20 30.85 1989-12-04 Landsat 5 TM 12:22 2.54 12:50 2.56 30 4.26 30.30 1987-11-13 Landsat 5 TM 12:26 2.35 13:00 2.37 30 7.8 31 1984-09-01 Landsat 5 TM 12:28 2.73 12:15 2.73 30 5.6 30.52
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images used were taken on the same season (dry season to minimize cloud cover) and during high tide (Table 1), hence, eliminating the TDE. The parameters for the selection criteria (climatological and tidal
data) were obtained from the Hydrography Department of the Brazilian Navy (DHN–Diretoria de Hidrografia e Navegação) and from Xtides software (https://flaterco.com/xtide/). The uncertainties were computed (Eq.2) based on the parameters of Eq. 2 (Romine et al., 2009), whose results are described in table 1.
𝐸𝑡 = √𝐸𝑟2+ 𝐸𝑝2´ (𝐸𝑞. 2)
TE: The total shoreline positional error for each year
RE: Rectification error
PE: Pixel error
Historical analysis of the shoreline - DSAS.
The DSAS is a freely available application software that works within ArcMap and are used to compute the rate-of-change for a time series of a given vector data (Thieler et al, 2017), which in this case is the shoreline. DSAS calculates the displacement over time base on three vector files: a baseline, the shoreline and transects perpendicular to both baseline and shoreline. The baseline is the starting point for all transects cast, while the transects intersect each shoreline vectors at the measurement points from which shoreline-change rates were calculated. The baseline was created onshore, positioned at 600 meters from the oldest shoreline (1984). Several transects with 100 meters spacing distance were generated on each of the defined sectors (Fig. 1), being 189 in sector I, 205 in sector II, 96 in sector III and 162 in sector IV.
Among all the DSAS methods, this study used Weghted Linear Regression (WLR) and WLR- squared (WLR²) to quantify changes along shoreline from 1984 to 2017. This method provide more reliable data since greater emphasis, or weight, is given towards determining a best-fit line, and the weight (Eq.3) is defined as a function of the variance in the uncertainty of the measurement (Thieler et al., 2017), according to the bias analysis. The WRL² statistics is a dimensionless index that ranges from 1.0 to 0.0 and measures how successfully the best-fit line accounts for variation in the data.
𝑊 = 1
𝑒² (𝐸𝑞. 3)
W: Regression’s weight e: Shoreline uncertainty value
Negative values of WLR indicates erosion of the shoreline, while positive denotes accretion. WRL² values above the threshold of 0.7 show that the regression analysis is consistent. Rates of accretion and erosion were divided into five categories, adapted from Luijendijk et al. (2018)
Table 2: Shoreline classification based on WLR’s values
Rates of shoreline change (m/yr) Shoreline classification
> + 3 Intense progradation + 0.5 to + 3 Moderate progradation - 0.5 to + 0.5 Stable - 3 to -1 Moderate retreat < - 3 Intense retreat
Results
Table 3 and Figure 3 show the results obtained from the WRL method. During the 34 years observed, 21% of the PRD coastline exhibited intense erosion, 30% moderate erosion, 4% stable, 30% moderate accretion and 15% intense accretion. Major erosion trended to have occurred away from PR mouth and predominantly on the west side of the delta, while deposition, or stability, prevailed on the east side. Considering the mean (-0.44 m/year) and median values (-0.49 m/year), the entire delta coastline can be classified as in stable conditions.
Table 3: Values observed from WLR and WRL² methods in the all the sectors of the study area.
Sectors Beaches Transects Mean
WRL Median WRL Std. WRL Mean WLR² Sector I Arrombado 1 - 54 -2.1 -2.4 0.6 0.75 Itaqui 55 - 80 2.4 2.4 0.6 0.71 Coqueiros 81 - 113 2.1 2.2 0.3 0.37 Atalaia 114 - 189 3.1 3 0.7 0.78
FERREIRA, T.A.B (2019) ANEXOS Sector II Eólica 1 - 97 -1.1 -3.0 1.2 0.75 Pedra do Sal 99 - 129 -2.2 -3.6 0.9 0.74 Cotia 130 - 146 1.8 2.5 1.8 0.75
Barra das Canárias 147 - 215 18.6 19.1 5.0 0.86
Sector III
Poldro's (Eastern Subsector) 1 - 52 -9 -4.8 5.2 0.86
Poldro's (Western Subsector) 53 - 97 1.8 2.3 1.0 0.78 Sector
IV
Caju's (Eastern Subsector) 1 - 31 5 8.0 6.7 0.69
Caju's (Central subsector) 32 - 102 -9.1 -10.0 3.3 0.91 Caju's (Western Subsector) 103 - 162 -6.3 -2 7.4 0.85 Sector I cover the coastline where are the beaches of Arrombado, Itaqui, Coqueiros and Atalaia, being located near the largest urban centers of the study area (Luís Correia and Parnaíba), with combined average population of 140,000 inhabitants. All the other sectors do not have major cities and / or villages near the coastline. In general, Arrombado was the only one in this sector to be classified as a moderate erosional beach (mean of -2.1 m/year), while all others registered moderate accretion. The highest rates of shoreline progradation occurred on Atalaia, the most urban beach, whose average values were +3.1 m / year. WRL² values have shown that the weighted regression provided reliable results for this sector, With the exception of Coqueiro’s beach (0.37).
According to the mean and median values, in sector II, the Eólica and Pedra do Sal beaches were classified as regions that were under moderate erosion. However, it was noticed that near the Igaraçu River (IR) mouth, occurred shoreline progradation, reaching values over 4 m/year. In turn, the beaches of Cotia and Barra das Canarias, near the PRD, showed moderate and intense accretion respectively. Regarding the coefficient of determination or WRL², all the beaches in this sector presented values above the threshold of acceptance, especially the Barra das Canárias (0.89), indicating that the changes of shoreline position were relatively constant.
Sector III corresponds to the Poldro’s beach, located in the west part of PRD. Due to the patterns of these transects, this region was divided into east and west subsectors. The east subsector showed that erosion was occurring in all its 52 transects, with a mean annual shoreline retreat of 9 m. In turn, the subsector west was classified as under moderate shoreline progradation, reaching a WRL an average of 1.8 m/year. In general, the mean WRL² on Sector III were above the threshold, with higher values occurring in the east subsector.
In Sector IV, as occurred in sector III, the Caju’s beach was divided into subsectors (east, central and west) due to the patterns of its transects. The subsector east showed intense accretion with mean shoreline progradation of 5 m/year. However, the low value of the WRL2 (0.69), which is slightly below
the threshold, indicates that this result may not be reliable. The transects from the central subsector (71) were classified as occurring intense shoreline retreat, with a WRL average of -9.1m/year. The same occurring in subsector west, in which was observed an average retreat of 6.3m/year. The high values of WRL² (0.9) show that erosion in subsectors central and west were constant between 1984 and 2017.
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Figure 3: Shorelines classification based on WRL values between 1984 – 2017 on all PRD (a), at sector I (b), sector II (c), sector III (d) and sector IV (e). The coefficients of determination (WRL²) values of these same sector are represented by (f), (g), (h) and (i)
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Discussions
Use of satellite images and statistical methods for analysis of coastal morphodynamics
The WRL² values in the four Sectors showed that the integration between statistical models, such as those used by DSAS software, and data obtained from satellite images are effective techniques for detection and quantification of variations on shoreline position with time. The simplicity of the method, its low costs for application and the accessibility of the input data (obtained from freely available satellite images), allow this methodology to be used on other areas worldwide (Ghoneim et al. 2015; Dada et al. 2018; Luijendijk et al. 2018; Mukhopadhyay et al. 2018; Qiao et al. 2018).
However, a number of uncertainties must to be considered in order to have accurate results from this method (Romine et al. 2009; Del Río and Gracia 2013). The choice of the remote sensor to be used, in regard of its special and spectral resolution, will depend on the magnitude of the changes intended to be measured. In this case, the Landsat program was chosen because it provided a large historical data, despit its moderate spatial resolution (range from 90 to 15 meters), and the changes to be mapped were well above its spatial resolution (hundreds to thousands of meters). Moreover, the spectral resolution of the Landsat helped on the clear definition of the land/water boundary (shoreline), allowing the use of automatic shoreline extraction methods such as AWEI (Fisher et al. 2016) and MNDWI (Xu 2006). This reduces the uncertainty related to the subjectivity of the interpreter in defining the shoreline position (Zhai et al. 2015; Fisher et al. 2016; Kelly and Gontz 2018). On areas where the magnitude of the changes are on the order of meters to few tenths of meters, sensors with high spatial resolution must be used (IKONOS, Quickbird, WorldView, aerial photographs), which are generally expensive and have a temporal limitation. The uncertainties related to the seasons and daily variatios of the tidal height influence the choice of the sensor in terms of its revisit period. Depending on the type of tidal regime (meso- and macro-tidal range) and beach slope, seasonal and tidal variations can introduce errors from tens to hundreds of meters in the statistical model (Gens 2010; Del Río and Gracia 2013; Aquino da Silva et al. 2019). It is worth to mention the importance on the choice of the statistical method to be used by DSAS. The Net Shoreline Movement (NSM) and EPR methods are recommended for scales of year-to-decade, while both regressions LRR and WRL are indicated for interannual to decadal time scales because the use a large number of shorelines data. (Thieler et al, 2017; Hapke et al., 2011).
Driving forces of shoreline change
On decadal time scales, the coastline is influenced by several interrelated factors, such as climatic (rainfall), hydrologic (river discharges), oceanographic (waves, currents and tides), geological and anthropogenic processes (Beatley et al., 2002). In the PRD, the highest values of accretion were observed near the IR and PR. This because although the PR presents a low suspended sediment concentration (SCC), around 91.4 mgl/l, the river yields approximately 2.54x106 t/year of sediments to inner continental shelf
(Aquino da Silva et al., 2015b). The majority of the sediment yielded is due to the high fluvial discharge of