Assessing coastal erosion and accretion trends along two contrasting subtropical rivers based on remote sensing data

Ocean and Coastal Management 169 (2019) 58–67

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Assessing coastal erosion and accretion trends along two contrasting subtropical rivers based on remote sensing data

T

L. Valderrama-Landerosa, F. Flores-de-Santiagob,∗ a

Subcoordinación de Percepción Remota, Comisión Nacional Para el Conocimiento y Uso de la Biodiversidad (CONABIO), 4903 Liga Periférico-Insurgentes Sur, Tlalpan, Cd., 14010, Mexico b Instituto de Ciencias del Mar y Limnología, Unidad Académica Procesos Oceánicos y Costeros, Universidad Nacional Autónoma de México, A.P. 70-305, Av. Universidad 3000, Ciudad Universitaria, Coyoacán, Cd., 04510, Mexico

A R T I C LE I N FO

A B S T R A C T

Keywords: Estuary Delta Shoreline change Mexico

The temporal shoreline position is a key indicator of coastal development and provides information on beach dynamics. Previous studies have indicated that there is a strong relationship between riverine sediment fluxes and beach volume. As such, worldwide hydroelectric projects and the associated damming infrastructure have decreased suspended sediment loads, and consequently increasing coastal erosion and deterioration of wetlands. The study was conducted along the coastline of the San Pedro River and the Santiago River on the Pacific coast of Mexico. Both rivers discharge into the ocean separated by 12 km of coastline, however the San Pedro River does not present dams through its watershed. Contrary, six dams had been constructed along the Santiago River since 1976. Results indicated that after 45 years, the San Pedro River coastline presented minimum variability and a total of 379 ha of accretion. Contrary, the Santiago River coastline showed major erosion trends with a total of 669 ha of beach and wetland losses. Our study highlights the feasibility of combining historical remote sensing data and GIS analysis in order to assess coastal variability trends when field-based surveys are not available.

1. Introduction Low-elevation coastal areas (i.e., shorelines) are natural dynamic features located at less than 10 m above sea level that mark the transition zone between land and sea (Boak and Turner, 2005). Although, they only cover 2% of the global land area, 10% of the world's human population lives along this region (Feagin et al., 2015). These areas are of utmost importance for a myriad of ecosystem services such as storm buffering (Ferreira et al., 2017), nutrient cycling (Schlacher et al., 2014), pollution removal (Giosan et al., 2014), carbon storage (Chen et al., 2017), nursery and feeding habitats for fauna (Buelow and Sheaves, 2015), and overall economic development including tourism (Escudero-Castillo et al., 2018) and cultural use (Nel et al., 2014). Despite their relevance, these regions are expected to become increasingly vulnerable to sea-level rise (Carrasco et al., 2016a,b), El NiñoSouthern Oscillation phenomenon (Barnard et al., 2015), and extreme events such as hurricanes and tsunamis (Feagin et al., 2015). In fact, many coasts of the world are suffering from severe erosion due to global and regional changes related to loss of sediment supply (e.g., Moussaid et al., 2015; Darwish et al., 2017; Kim et al., 2017), construction of coastal infrastructure (e.g., Kermani et al., 2016; Selvan et al., 2016),

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and urbanization (e.g., Almonacid-Caballer et al., 2016; Martinez et al., 2017). Hence, extraction and quantification of shorelines to monitor coastal zones for national development and environmental protection is a key endeavor (Deng et al., 2017). Coastal researchers, engineers, and managers require accurate information about the actual position of the shoreline and possible predictions in the future, mainly based on previous trajectories, in order to take optimal management decisions. Hence, the study and analysis of erosion and accretion trends in shorelines through time is of utmost importance for a wide range of coastal issues and projects, such as coastal management (e.g., Schlacher et al., 2014; Castedo et al., 2015) and conservation (e.g., Cruz-García et al., 2015), beach erosion (e.g., Bheeroo et al., 2016; David et al., 2016), estuaries assessment (e.g., Cellone et al., 2016; Leuven et al., 2016), monitoring coastal vulnerability (e.g., Tahri et al., 2017), assessing the possible effects of sealevel rise (e.g., Dean and Houston, 2016; Purkis et al., 2016), and empirical or predictive modeling of coastal hydrodynamics (e.g., McLoughlin et al., 2015; Anastasiou and Sylaios, 2016; Deng et al., 2017). However, monitoring shorelines variability over optimal scales is a difficult task concerning several considerations about the dynamics involved over temporal scales (García-Rubio et al., 2015).

Corresponding author. E-mail address: fl[email protected] (F. Flores-de-Santiago).

https://doi.org/10.1016/j.ocecoaman.2018.12.006 Received 21 November 2017; Received in revised form 20 August 2018; Accepted 7 December 2018 0964-5691/ © 2018 Elsevier Ltd. All rights reserved.

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(Xu et al., 2016). These errors related to digitalizing and processing affect the accurate detection of shoreline position (Cellone et al., 2016). For instance, the most commonly employed shoreline detection technique has been subjective visual interpretation and manual digitalization of aerial photographs. Alternatively, recent digital photogrammetry methods and digital image processing techniques, such as information from the near-infrared wavebands (NIR) (e.g., Yu et al., 2011), synthetic aperture radar (SAR) (e.g., Singhroy, 1996; SouzaFilho and Paradella, 2003), and light detection and ranging (LiDAR) (e.g., Almonacid-Caballer et al., 2016) are particularly useful for shoreline mapping, as they provide a clear contrast between land and water (Gens, 2010). The coastline position derived from active sensors such as LiDAR and SAR data, as well as passive sensors like IKONOS and WorldView has fine spatial resolution, but the cost is high for frequent observations over large areas. Satellite data obtained from the Moderate Resolution Imaging Spectroradiometer (MODIS) has daily resolution and globalscale coverage, but its spatial resolution (e.g., 250 m, 500 m, and 1 km) is too coarse to assess the slight variations along coastlines. Hence, Landsat data with moderate temporal resolution (16 days) and medium spatial resolution (30 m/pixel) have been useful than other remote sensing data sources for monitoring coastline dynamics at large scales (Li and Gong, 2016; Wulder et al., 2016). Moreover, Landsat images constitute a major data source for coastal monitoring since the launch of the first satellite in 1972, providing broad-scale information on coastal patterns (Cruz-García et al., 2015). Additionally, it also has the potential advantage of assessing shoreline variability in remote places with minimum in situ coastal information (García-Rubio et al., 2015). Initially, shorelines are typically delineated using NIR wavebands and vectorized for further analysis undertaken within a Geographic Information System (GIS). In fact, the most commonly applied method involves calculating shoreline change rates from a number of shoreline vector files derived from remote sensing data sources collected in different dates (Ford, 2013). Hence, remote sensing data and GIS methods could provide valuable estimates of shoreline variability, which enables historical rates of change to be quantified and the possible processes that have affected coastlines. Considerable effort has been devoted to studying erosion and accretion trends along shorelines (see reviews by Silva et al., 2014; Barnard et al., 2015; Ranasinghe, 2016; Ferreira et al., 2017). Specifically, Darwish et al. (2017) assessed geomorphic changes along the Nile Delta coastline between 1945 and 2015 using topographic maps and Landsat satellite images. Results provided the first evidence about the erosion processes occurring in this delta after the construction of the Aswan High Dam. Kim et al. (2017) determined shoreline variations along two beaches in the Republic of Korea between 1975 and 2013 using aerial photographs and an image from the KOMPSAT-3 satellite. Results showed a clear erosion event due to the presence of a dam that decreased the discharge volume of suspended solids from the watershed. Kermani et al. (2016) estimated historical changes in shoreline position of the Bay of Jijel (Algeria) occurred between 1960 and 2014 using aerial photographs and a QuickBird satellite image. Results indicated that most significant shoreline changes occurred after the construction of a dam and a port. David et al. (2016) studied shoreline changes along the Van Island (Gulf of Mannar) during the period from 2000 to 2016 using Landsat satellite data. Results revealed that the Van Island will vanish in the next 25 years if current environmental conditions are maintained. Cellone et al. (2016) used a combination of remotely sensed data (aerial photographs, GeoEye-1, IKONOS, and WorldView-2) to retrieve shoreline positions between 1943 and 2013. Results predicted coastline erosion at a rate of 4 m/year for the next 50 years. Bheeroo et al. (2016) investigated the shoreline erosion risk in Mauritius using aerial photographs (1967–2012). Results denoted the occurrence of severe erosion along the coastline but the coastal segment located in front of the protective coral reef area presented minimum erosion. Ayadi et al. (2016) evaluated shoreline erosion risk along the

The most commonly measured physical properties for beach systems included aspects of size, geometry, and sediment characteristics of the shore (Schlacher et al., 2014). However, the most noticeable aspect for morphological variability is the position of shoreline, which represents a change width on the beach (Ahn et al., 2017). As such, the shoreline position changes continually through time due to cross-shore and alongshore sediment dynamics within the littoral zone. In this regard, the dynamic nature of shorelines depends on several aspects at the costal boundary such as waves, tides, groundwater, storm surge, and river input (Boak and Turner, 2005). It is well known that the principal coastal sources of sediment transport (Komar, 1998) and carbon flux (Li et al., 2017) are rivers and streams. Hence, climate change and land-use modifications along the upper watershed could affect the supply of sediment to beach and, consequently, increase shoreline erosion (Kim et al., 2017). In fact, during the last century, reduction in river sediment supply is considered a global concern resulting in major erosion processes that now affect many shorelines (Carrasco et al., 2016a,b). Consequently, coastal populations are becoming more prone to extreme flooding from tropical storms and hurricanes (Ferreira et al., 2017), and changes in sediment supply can influence the benthic environment of coastal lagoons and estuaries (Kim et al., 2017). Therefore, coastal management decisions should be based on temporal empirical data according to the particular aim of the investigation, presentable in a form that can be interpreted by non-scientist (Schlacher et al., 2014), and nothing works better than classification methods of spatial data. The spatial extraction of shoreline is a useful endeavor for several applications such as coastline change detection, coastal zone management, and flood prediction (Moussaid et al., 2015). Potential data sources for shoreline assessment include historical maps and charts, in situ geographic positioning surveys, aerial photography, and several types of image data derived from remote sensing platforms (Boak and Turner, 2005). Overall, two main approaches have been used for shoreline change detection. (1) Direct shoreline observations (i.e., fieldbased methods) typically involving topographic survey techniques such as repeated vertical height measurements along perpendicular profiles on the beach (e.g., Carrasco et al., 2016a,b). (2) Analysis of a time series of shoreline positions interpreted remotely, most commonly from aerial photographs (e.g., Bheeroo et al., 2016), satellite images (e.g., Darwish et al., 2017), and unmanned aerial vehicles (e.g., Casella et al., 2016). Although the former method is known to be the most reliable approach to identify vertical characteristics of the shoreline, such method requires a lot of time and money in case of a wide area, and thus the sampling frequency often comes at the cost of limited spacing coverage (Ahn et al., 2017). To overcome these limitations, the remote sensing method has the advantage of monitoring coastline dynamics over a variety of spatiotemporal scales. However, the choice of remotely sensed data for large-scale coastline observations depends on the available data at the specific location, the temporal frequency, and the spatial resolution (Li and Gong, 2016). Reasonable quality vertical stereo aerial photographs became available until the late 1930 providing useful visual coverage of the coast (Jensen, 2016), but temporal resolution was very site specific. In general, aerial photographs present some sort of distortion and must be corrected before spatial assessments. Most commonly issues included radial distortion, unstable tilt and pitch of the aircraft, and inconstant scale by changes in altitude along the flight line (Boak and Turner, 2005). However, if available, aerial photographs are the most common data source for determining historical shoreline morphology. Unlike aerial photographs, satellite data has developed rapidly over the past few decades providing near-continuous monitoring of many shorelines around the world (Gens, 2010). Remotely sensed images have been extensively used to calculate erosion and accretion processes along shorelines by comparing multiple images at different acquisition times (e.g., Almonacid-Caballer et al., 2016; Ayadi et al., 2016; Darwish et al., 2017). However, the accurate detection of the coastline position is affected by tidal conditions and the shoreline limit selected by the user 59

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First step consisted on data acquisition and correction, second stage, digitalization of the coastline and, finally, determination of shoreline variability.

Bejaia bay (Algeria) based on a series of aerial photos, satellite images, and topographic surveys (1960–2013). Results illustrated shoreline variations during the last 60 years with erosion intervals between 1.75 and 7.89 m/year. Considering the aforementioned studies, it is clear that ultimately, coastal geomorphology would likely suffer accretion and erosion processes specially in estuarine systems under the influence of upper watershed constructions. To cope with these problems, monitoring coastline dynamics is of utmost importance, particularly through the use of remote sensing data. Hence, the aim of this study was to examine the extent to which the observed shoreline has changed along two contrasting subtropical river systems (San Pedro River and Santiago River) between 1970 and 2015. The San Pedro River presents no constructed dams along its watershed while the Santiago River has six dams since 1976. The primary objective was to map the shoreline over several elapsed times and quantify erosion or accretion trends using aerial photographs and satellite data.

3.1. Data acquisition and shoreline extraction Historical aerial photographs were acquired from the “Instituto Nacional de Estadística y Geografía (INEGI, Mexico)” analog record and the ICA Foundation. Landsat data were downloaded via earthexplorer. usgs.gov. SPOT-5 data were acquired through the “Estación de Recepción México de la Constelación SPOT (ERMEX) - Secretaría de Marina (SEMAR, Mexico).” Each aerial photograph and satellite image (Table 2) was orthorectified and georeferenced separately, and control points (e.g., infrastructure and roads) were selected by comparing each image with digital cartography from INEGI, Mexico. The digital cartography consists on vector information within a constantly updated GIS platform including rivers, roads, agricultural fields, wetland, terrestrial vegetation, urban settlements, and other geographical features. The air photographs were scanned, adjusted for scale, and their coastline was digitalized using the traditional visual interpretation method for analog data (Boak and Turner, 2005). The NIR waveband was used to separate land from sea in the Landsat and SPOT-5 images. The discrete shorelines were extracted and vectorized into shapefile format in ArcMAP v10.3.

2. Study area The San Pedro and Santiago watershed systems are part of Marismas Nacionales, an environmentally crucial complex located between the Sinaloa and Nayarit states along the Pacific coast of Mexico (Fig. 1). This system presents several coastal lagoons and the larges extension of mangrove forests found along the Pacific coast of the American continent. The coastline presents a sub-humid climate with mean annual temperature of 23 °C (INEGI, 2016). The annual total precipitation, which occurs between the months of June and October, ranges between 1200 and 1500 mm (Cruz-González, 2012). The flooding pulse is seasonally marked and it is influenced by the semi-diurnal tidal regime with a maximum peak between 0.7 and 0.85 m above sea level (http:// predmar.cicese.mx/). The ocean waves present maximum energy during the hurricane season (i.e., July through October) with an overall northeastern direction (Cruz-González, 2012). The influence from the San Pedro and Santiago rivers is concentrated at the southern part of the Marismas Nacionales coastline. Both rivers discharge directly into the Pacific Ocean separated by 12 km of beach deposits. However, it has been suggested that the construction of six dams along the Santiago River (Table 1) has reduced the available sediment source for the entire coastal zone (Ortiz-Pérez and RomoAguilar, 1994; Blanco-Correa, 2011; Cruz-González, 2012; MartínezMartínez et al., 2014). Contrary, the San Pedro River does not present constructed dams and flows directly to the ocean through coastal lagoons and marshes, contributing with suspended solids and sediments (Hernández-Guzmán et al., 2016). The San Pedro River belongs to the San Pedro-Mezquital basin covering an area of 27,674 km2. It is 540 km long with an average annual flow of 2691 hm3 at the mouth of the river in the Pacific Ocean (WWF, 2013). The Santiago River belongs to the Lerma-Chapala-Santiago River basin, which covers a total area of 137,144 km2 (Ramírez-García et al., 1998). The Lerma River (750 km long) is born in Mexico's central high plateau surprising 3000 m above sea level and ends in Lake Chapala at 1510 m above sea level (Hansen and van Afferden, 2001). The Santiago River is 450 km long from Chapala Lake to the Pacific Ocean with a watershed extension of 76,400 km2 (Ramírez-García et al., 1998). The Lerma-Chapala-Santiago River basin accounts for more than one-third of the country's economic activity, one fifth of all commerce, and one-eighth of the nation's agricultural land (Hansen and van Afferden, 2001). Historical data showed an average discharge of 2000 m2/s/year and a composition of 0.4% of suspended material along the mouth of the Santiago River before the construction of the Aguamilpa and San Rafael dams (Martínez-Martínez et al., 2014).

3.2. Historical shoreline analysis - DSAS The computer software Digital Shoreline Analysis System (DSAS) was developed to determine shoreline rates-of-change positions using a time series of vectorized shoreline data residing in a GIS environment (Thieler and Danforth, 1994). DSAS v4.03.4730 is an extension tool of ArcGIS v10.3 software, which was developed jointly by the United States Geological Survey (USGS) and the TPMC Environmental Services. DSAS model automatically generates transect lines perpendicular to the coastal baseline and across the shoreline at a user-specified spacing. Each transect-shoreline intersection along this baseline is then used to quantify rate-of-change statistics. In this analysis, 254 transects were generated at a 100 m spacing distance, and about 1.5 km perpendicular to the baseline according to Valderrama-Landeros (2019). It is beyond the scope of this article to explicitly review existing shoreline extraction methods, but several reviews on such approaches exist (e.g., Boak and Turner, 2005; Gens, 2010; Silva et al., 2014) and references therein. The movement of shoreline with respect to the baseline is considered seaward shift (accretion) and landward shift (erosion) at each transect, and the statistical values have been denoted as positive for accretion and negative for erosion. The DSAS model executes five statistical operations in order to quantify shoreline variability, including the Shoreline Change Envelop (SCE), Least Median of Squares (LMS), Linear Regression Rate (LRR), Net Shoreline Movement (NSM), and End Point Rate (EPR) (Thieler and Danforth, 1994). Among these five computational operations, the EPR calculates the shoreline movement rate between two consecutive images, and the NSM displays the average covered distance among the historical images. The total changes in shoreline movement were represented for all available shoreline positions, and the coastal rates of erosion and accretion were quantified with respect to the baseline position. 4. Results Fig. 2 depicts the total accretion and erosion areas between 1970 until 2015. The overall erosion area is 28% more compared to the accretion zone in the study site. Specifically, the coastline located at the San Pedro River mouth (North) presents 379 ha of accretion, while the coastline at the south of the Santiago River shows 669 ha of erosion. The highest classification losses at the Santiago River mouth were wetland vegetation (55%) followed by beach deposits (34%), and the

3. Material and methods The shoreline assessment approach comprised a three-stage process. 60

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Fig. 1. Study site at the southern location of the Marismas Nacionales complex. (a) San Pedro River and Santiago River watershed locations (white area). The northern yellow circle indicates the San Pedro mouth while the southern circle the Santiago River mouth. Red circles indicated water reservoirs along the Santiago River: Amado Nervo dam (1976), San Rafael dam (1994), Aguamilpa hydroelectric dam (1993), El Cajón hydroelectric dam (2006), and La Yesca hydroelectric dam (2012). (b) Landscape classification map based on INEGI (2016) data (http://www.beta.inegi.org.mx/temas/mapas/ topografia/). (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)

October (Fig. 3). The entire EPR interval (1970–2015) is depicted in Fig. 4. In general, the San Pedro River transects presented minimum variability with an overall accretion rate as high as 33 m/year between 1986 and 1990 (Fig. 4a). Contrary, the Santiago River transects showed major variability between accretion and erosion trends prior to the 1995 satellite data. During this period, an average accretion pattern was found but some punctual locations close to the Santiago River mouth presented accretion rates as high as 91 m/year, 76 m/year, and 30 m/year during the 1980–1986, 1986–1990, and 1990–1995 intervals, respectively. After the 1995 satellite data, the Santiago River shoreline erosion rate intensified reaching a maximum EPR of −122 m/year between 2000 and 2005. An additional accretion process was presented only between the 2005 and 2010 data with 42 m/year, but the overall development consisted of an erosion trend (Fig. 4b). Fig. 5 showed the specific accretion and erosion NSM distance (m) from the San Pedro River and the Santiago River mouths. Overall, the San Pedro River shoreline presented major variability in NSM with an overall seaward shift (accretion); however maximum landward shift (erosion) was found at 6.5 km south of the San Pedro River mouth

Table 1 Water reservoirs characteristics along the Santiago River watershed based on CONAGUA (2017) report. Reservoir name

Inauguration year

Levee height (m)

Levee length (m)

Total capacity (hm3)

Amado Nervo San Rafael Aguamilpa El cajón La Yesca Santa Rosa

1976 1994 1993 2006 2012 1964

ND 38.5 186 186 220 114

ND 380 660 640 628 150

ND ND 5540 2282 2293 403

remaining 11% corresponded to non-wetland vegetation. Contrary, 60% of the accretion area is represented by sand deposits, followed by agricultural fields (19%), terrestrial vegetation (10%), non-forested wetlands (9%), and anthropogenic development (2%). The monthly average surface circulation pattern shows a littoral current with a southern direction. However, there is a northern circulation pattern at 106° W during the hurricane season between the months of July until 61

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Table 2 Remote sensing data collected for the DSAS analysis. Estimated mean sea level (MSL) based on software http://predmar.cicese.mx/. Data source

Date of acquisition (mm/dd/yyyy)

Time of acquisition (GMT)

Estimated MSL (m) at collection

Pixel spacing (m)

RMSE (m)

Air photo Landsat Landsat Landsat Landsat Landsat SPOT-5 SPOT-5 SPOT-5

ND/ND/1970 10/12/1980 03/19/1986 04/15/1990 04/13/1995 04/26/2000 ND/ND/2005 01/18/2010 11/26/2015

ND 16:44 16:50 16:44 16:33 16:59 ND ND ND

ND +0.60 −0.41 −0.13 +0.07 −0.39 ND ND ND

4.5 60 30 30 30 30 10 10 10

<1 31.5 4.6 4.8 4.9 4.8 <1 <1 <1

section of the Santiago River mouth, the coastline showed an almost identical distance (∼1000 m) between the accretion and erosion processes, but overall coastline distance losses were intensified during the 1995–2000, 2000–2005, and 2010–2015 data (Fig. 5b).

between data acquired in 2010 and 2015. Additionally, less than 100 m of beach erosion was found close to the San Pedro River mouth oscillating between 1970 and 2015; however 250 m of beach was deposited during the same elapsed time. The south part of the San Pedro River coastline showed a maximum seaward shift of 390 m (Fig. 5a). Contrary, the Santiago River shoreline presented a unique pattern where the south section of the river mouth showed an exceptionally marked landward shift of 1000 m between 1970 and 2015. Regarding the north

5. Discussion Tropical coastal areas located along estuaries are very sensitive to

Fig. 2. Overall erosion and accretion areas along the San Pedro and Santiago Rivers coastline based on DSAS analysis (1970–2015). 62

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Fig. 3. Monthly average surface currents directions for the year 2015 based on sea-surface elevation data. Black circle represents the San Pedro River mouth while hollow circle represents the Santiago River mouth locations.

sea-level rise, storm surge from hurricanes, continental fluvial discharges, and along-shore sediment transport due to the seasonal littoral current (Ghosh et al., 2015). It is expected that global climatic patterns could exacerbate the current situation of coastal areas by enhancing degradation caused by the aforementioned processes. The shoreline is the most rapidly changing landform along coastal areas, and thus its temporal position is a key indicator of coastal variability and provides the most optimal information on coastal dynamics (Moussaid et al.,

2015). There is a strong correlation between beach volume and riversupplied sediment in coastal dynamics (Kim et al., 2017). As such, damming construction could decrease suspended sediment loads within the river resulting in an increase of coastal erosion, and deterioration of coastal marine ecosystems (Mahapatra et al., 2014; Moussaid et al., 2015). In fact, the Santiago River mouth wetland established in the alluvial plain is subject to sediment inputs from the river, and to the sediment exchange due to the littoral current and waves (Ramírez-

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Fig. 4. End Point Rate (EPR) (m/year) along the San Pedro River (a) and Santiago River (b) shorelines. Negative values represent erosion rate while positive values accretion rate.

The method applied in this study was found to be an effective approach for detection and quantification of shoreline position changes along two estuarine coasts. Our study showed that the detection of shoreline evolution can be attained by using corrected aerial photographs, moderate spatial resolution Landsat data, high spatial resolution SPOT data, and GIS-based analysis. It also suggested the advantage of using DSAS as a fast automatic calculation approach to compute shoreline variability. However, there are a number of concerns that need to be considered when using remote sensing techniques for delineating shorelines. For instance, the main limitations of our method are the different spatial resolutions, data cost, and tidal variations. Regarding spatial resolution and data cost, it is well known that the only available historical remote sensing data are aerial photographs and coarse spatial resolution Landsat images (Wulder et al., 2016) since the first satellite launched in 1972 (Boak and Turner, 2005). Contrary, very-high spatial resolution data (e.g., WorldView, IKONOS, and QuickBird) are expensive and generally limited in terms of temporal variability. Often, it is not possible to perform this either for logistical or cost constraints, thus reducing the reliability of the detection of shoreline changes in some parts of the World where minimum coverage is available, and thus exacerbating optimal shoreline detection and quantification. Nevertheless, it has become apparent, particularly through considerable shoreline loss (∼1000 m), that coastal detection in this study site was not unhampered by coarse spatial resolution satellite data. Moreover, our results are similar to those reported by CruzGonzález (2012), and those previously published by Ortiz-Pérez and Romo-Aguilar (1994) regarding fluvial geomorphology along the Santiago River. Concerning tidal variability among the remote sensing data sources, it has been suggested that tidal amplitude plays a key role during veryhigh spatial resolution aerial surveys while it can be safely neglected for spaceborne data acquisition due to coarser spatial resolution of the satellite images (Gens, 2010). Even though it is very unlikely that spaceborne data could be acquired during the same tidal amplitude, we believe our results were not hinder by the different tidal amplitudes depicted in Table 2. For instance, our study site is classified as a

García et al., 1998). Based on historical remote sensing data, our results indicated that the variability of shoreline positions over a mid-centennial period (45 years) was subject to either accretion or erosion processes depending on the rivers influence. Specifically, this study illustrated that, during this elapsed time, 64% of the coastline (669 ha) had been eroded at the southern part of the Santiago River mouth which presents six constructed dams along its watershed. Contrary, 36% (379 ha) had been deposited along the San Pedro River mouth which is unhampered by dams. Although both rivers are not identical, and there is a lack of historical in situ suspended solids and river volume data, it is apparent that the erosion trend was intensified after the construction of the major hydroelectric dam of Aguamilpa in 1993 according to the EPR diagram. It is important to mention that natural factors such as storm surge by hurricanes and the seasonal littoral current could affect the erosion and accretion trends. According to the data provided by the NOAA, from 1970 to 2015, seven tropical storms and hurricanes have passed within 100 km of the study area causing impacts on the coastline. The two events that made landfall on the Santiago River estuary were the tropical storm Priscilla in 1971, which could explain in some way the lack of accretion in the EPR between 1970 and 1980, and hurricane category 4 Kenna in 2002, which explains the maximum EPR erosion trend of −122 m/year between 2000 and 2005. However, hurricane Kenna landed at the southern part of the Santiago River and it appears that the hurricanes influence did not affect the San Pedro River shoreline since no apparent changes in EPR were found. To the best of our knowledge, there is no historical data regarding littoral current dynamics in the study area, however sea-level elevation models depicted in Fig. 3 show surface circulation vectors with a constant southern direction. Based on these littoral current trajectories, it appears that the sediment load from the San Pedro River does not reach the Santiago River mouth, which could explain the higher erosion area at the southern part of the Santiago shoreline. Interestingly, prior to the 1995 data, the higher NSM variability between erosion and accretion trends consisted on a sandbar formation at the north section of the Santiago River mouth. However, this sandbar showed an overall erosion trend after the 1995 data. 64

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Fig. 5. Net Shoreline Movement (NSM) (m) along the San Pedro River (a) and Santiago River (b) shorelines. Negative values represent erosion while positive values accretion. Vertical black arrows indicate both rivers mouth locations. Horizontal black arrows indicate shoreline direction from the river's mouth.

in the NSM diagram were higher than the minimum spatial resolution from the Landsat data. The decision of using DSAS statistical parameters in our study enabled an exploratory assessment of the temporal and spatial dynamics of the coastal change because of their ability to use all of the shoreline positions (1970–2015). Hence, shoreline delineation by standard classification methods, based on image processing techniques, can reduce the time and cost of traditional shoreline detection surveys if no historical in situ data is available (Sekovski et al., 2014). Understanding historical shoreline variability that contributes to the sediment supply and transport within the river watershed has major implications for the effective management of coastal environment. Although our results are only based on remote sensing data, they are expected to provide more insight into future coastal management decisions and sustainability development planning in subtropical estuarine areas.

continental collision coast (Inman and Nordstrom, 1971) presenting an abrupt foreshore height (∼1 m) according to the beach topographic profiles by Cruz-González (2012). Consequently, the concomitant development of a narrow continental shelf and semi-diurnal tidal amplitude with a maximum peak of 0.85 m above mean sea level could result in a minimal influence by tidal differences using coarse spatial resolution satellite data. Additionally, both river mouths are located nearby and thereby tidal dynamics should be similar. However, not all locations worldwide could be suitable for this approach. For instance, shoreline delineation accuracy from remote sensing images could be hampered along marginal sea coasts with a low foreshore slope and strong tidal currents. In the end, however, in the absence of long-term monitoring endeavors through cross shore profile surveys and volumetric analysis, seasonal shoreline mapping proves to be a powerful tool to understand a particular shoreline evolution (Bheeroo et al., 2016). Moreover, the use of the NIR waveband has proven to be a suitable method for automatic water extraction in satellite images (Jensen, 2016), and thus decreasing the subjective approach of visual interpretation. Finally, the large erosion and accretion distances found

6. Conclusions Coastal erosion is a major problem that affects several estuarine 65

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areas worldwide. In fact, shoreline position reflects the coastal sediment budget, and modifications may indicate natural or anthropogenic effects alongshore. To our advantage, shoreline detection and quantification have improved with the availability of new image capture, processing, and analysis technology. The usual lack of in situ coastal survey data can be overcome by the use of remote sensing methods, which have the main advantage of providing historical acquisition image archives. Therefore, accurate detection and frequent monitoring endeavors of shorelines are of utmost importance to understand coastal processes. To our knowledge, this study provides the first quantitative analysis of shoreline variability of the San Pedro River and the Santiago River between 1970 and 2015. The overall erosion (64%) and accretion (36%) areas were detected and quantified using remote sensing data and automatic GIS calculations (i.e., DSAS). The shoreline trends were measured according to eight elapsed times: 1970–1980, 1980–1986, 1986–1990, 1990–1995, 1995–2000, 2000–2005, 2005–2010, and 2010–2015. The study revealed highly distinct results on spatial and temporal scales. Specifically, maximum coastal erosion was recorded at the southern part of the Santiago River mouth. Contrary, maximum accretion area was recorded along the San Pedro River mouth. This method, based on historical remote sensing data and automatic GIS analysis, could be used for a wide range of coastal areas when no in situ data is available. Nevertheless, it is important to understand the specific coastal geomorphology and tidal dynamics for each study site in order to optimally delineate the shoreline position with remote sensing data as previously discussed.

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