ABSTRACT

Finkl, C.W. and Makowski, C., 2021. Alongshore classification and super domain delineation of coastal belts based on interpretation of biophysical catenary sequences observed from satellite images. Journal of Coastal Research, 37(1), 1–25. Coconut Creek (Florida), ISSN 0749-0208.

The cross-shore classification of coastal belts, facilitated by the interpretation of biophysical features from satellite imagery, is an adjunctory approach to traditional alongshore classification procedures. Methods such as the Biophysical Cross-shore Classification System (BCCS) (Finkl and Makowski, 2020a) and the Coastal Belt Linked Classification (CBLC) (Finkl and Makowski, 2020d) not only provide insight into cross-shore natural ecological variability and zonation, but also emphasize eco-geomorphological linkages so that shore-normal transitions and successions can be delineated and understood. The discernment of numerous cross-shore biophysical transect codifications in a satellite image seascape scene (referred to as either BCCS or CBLC code sequences) results in coastal belt segmenting that identifies offshore, nearshore, inshore, and onshore similarities and differences. This, in turn, can be correlated alongshore by establishing the swath widths of each cross-shore transect as an individual domain. Data integration and interpretation processes are similarly applied as procedural management of geological borehole (well) logs to determine drilling and petrophysical parameters. This study demonstrates that once all individual alongshore domains are interpreted for a specific coastal area, they can be collectively amalgamated and concised to create a universal alongshore super domain for the entire coastal belt. The advantage of constructing a Concised Alongshore Super Domain is that the procedure provides a sagacious and rational basis for swath alongshore classification of coastal belts. This study shows the effective use of this methodology across coastal belts of varying latitudes, as the interpolation of shore-normal ecological sequences validates the cross-shore correlation between transects and can result in a universal alongshore classification process in the form of super domains for coastal belts worldwide.

INTRODUCTION

Because coasts constitute some of the most complicated environments on Earth, they have been the subject of human inquiry since antiquity. Some coastal segments are known for their stunning beauty and salubrious landscapes that are amenable to development and habitation, while other stretches are fearsome to seafarers and generally inhospitable to human activities due to a variety of biophysical factors. Whatever their lure, precautionary, or avoidance factors are, the elucidation of coastal environments has always intrigued early navigators and explorers while more recently many coastal researchers are able to focus, without divagating, on better understanding and appreciating the value of classificatory efforts. There are myriad approaches to coastal classification, with most efforts being of a specific purpose in relation to nature. Such myopic approaches are justified by the fact that most coastal environments are so intricate that some degree of simplification is required for the elemental structure comprehension of individual segments. That is, the individuation of the coastal continuum results from classification of “individuals” and their associative occurrences (e.g., eco-geomorphological features, such as beaches, cliffs, coral reefs, dunes, flats, lagoons, etc.) that comprise distinctive and specific stretches of the world's coasts.

Purpose and Goals

The purpose of this paper is not meant to be a critique of prior coastal classification efforts but, on the other hand, an exploration of possibilities for the application of using cross-shore transect codifications to classify alongshore swaths of coastal belts, which are here referred to as “domains”. This term is used in preference to traditional one-dimensional (1D) alongshore designations because it incorporates both an undetermined distance offshore and inland, as determined by natural eco-geomorphological features. As explained by Finkl and Makowski (2020a,b,c,d), the variable distances offshore and inland from the land's edge are determined by the nature and sequences of specific types of biophysical features that are repeatable the world over and thus referred to as archetypes. These main archetypes can then be subdivided in terms of greater specificity as sub archetypes (Table 1). A main archetype term might, for example, be categorized as a “beach”, of which a sub archetype might be interpreted as being of carbonate or silicate mineralogical grain composition. By applying these archetype interpretations, one can then explore the possibility of using cross-shore transect ecological sequences to characterize whole alongshore segments using the concept of domains. The ultimate goal is to differentiate, interpret, and classify shore-parallel swaths of coastal belts based on the concision of cross-shore transects in the form of a Concised Alongshore Super Domain.

Table 1

Code definitions of archetypes and sub archetypes using bolded upper- and lowercase letters as primary archetype designators and lowercase alphabet subscripts as secondary sub archetype refinements to indicate the composition and nature of barriers, beaches, beach ridges, cliffs, coral reefs, deltas, dunes, flats, ice, lagoons, mountains, rock, till (glacial material), uplands, and wetlands. Numerals are provided for shore-parallel configuration terms (overall alongshore coastal belt configuration in planview). This table is modified from Finkl and Makowski (2020a).

Code definitions of archetypes and sub archetypes using bolded upper- and lowercase letters as primary archetype designators and lowercase alphabet subscripts as secondary sub archetype refinements to indicate the composition and nature of barriers, beaches, beach ridges, cliffs, coral reefs, deltas, dunes, flats, ice, lagoons, mountains, rock, till (glacial material), uplands, and wetlands. Numerals are provided for shore-parallel configuration terms (overall alongshore coastal belt configuration in planview). This table is modified from Finkl and Makowski (2020a).
Code definitions of archetypes and sub archetypes using bolded upper- and lowercase letters as primary archetype designators and lowercase alphabet subscripts as secondary sub archetype refinements to indicate the composition and nature of barriers, beaches, beach ridges, cliffs, coral reefs, deltas, dunes, flats, ice, lagoons, mountains, rock, till (glacial material), uplands, and wetlands. Numerals are provided for shore-parallel configuration terms (overall alongshore coastal belt configuration in planview). This table is modified from Finkl and Makowski (2020a).
Table 1

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Description of the Geographical Study Areas

Study examples are derived from five satellite images that occur in a range of latitudinal zones spanning polar, subpolar, middle latitude, subtropical, and tropical coastal belts. The polar example is from the northern coast of the Russian Federation, facing the Laptev Sea on the Gulf of Anabar. The subpolar coastal satellite image faces the Gulf of Finland, an extension of the Baltic Sea, on the southwest coast of Estonia. The middle latitude satellite image is located on the eastern Pacific coast of central-western Oregon in the United States. The subtropical satellite image occurs on the east coast of Queensland, Australia, facing the Coral Sea. The tropical satellite image occurs in Gambia, Western Africa, on the eastern Atlantic coast. These study areas were selected to provide a range of examples from different latitudes and Köppen-Geiger climatic zones so that their alongshore domains, based on cross-shore transect catenary associations, could be compared and contrasted. Additional features, characteristics, and geographical parameters, such as latitude and longitude, the Köppen-Geiger climate designation (e.g., Peel, Finlayson, and McMahon, 2007), large marine ecosystems (LME) (e.g., Sherman, Aquarone, and Adams, 2009), and ecoregion/biome (ER) (e.g., Bailey, 1998; Dinerstein et al., 2017) of these individual coastal belts are summarized in Table 2.

Table 2

Summary table from five different latitudinal zone study areas, including coastal belt location, latitudinal zonation, latitude and longitude coordinates, climate classification, Large Marine Ecosystem (LME) designation, the associated terrestrial Ecoregion (ER) and biome, and the Concised Alongshore Super Domain.

Summary table from five different latitudinal zone study areas, including coastal belt location, latitudinal zonation, latitude and longitude coordinates, climate classification, Large Marine Ecosystem (LME) designation, the associated terrestrial Ecoregion (ER) and biome, and the Concised Alongshore Super Domain.
Summary table from five different latitudinal zone study areas, including coastal belt location, latitudinal zonation, latitude and longitude coordinates, climate classification, Large Marine Ecosystem (LME) designation, the associated terrestrial Ecoregion (ER) and biome, and the Concised Alongshore Super Domain.

Russian Federation Coastal Belt (Laptev Sea, Gulf of Anabar Coast)

This satellite image was acquired on 12 December 2015, and located at 73°41′34″ N by 115°27′28″ E. The polar site lies about 65 km northeast of the Anabar River, about 36 km east of the Mus-Khaja River mouth, about 400 km west of the Lena River, and 280 km west of the Lena River Delta (World Map, 2019). Cross-shore transects extend about 2 km inland from the Gulf of Anabar on the northern coast of the Sakha Republic (Yakutia) Administrative Division in the Russian Arctic where the basic sequences repeat for about 11 km inland in the promontory. On the flanks of the classified coastal belt, tundra reaches the shore with scattered barrier island beaches, lagoons, and wetlands. Unusual set ups include deltaic tidal flats fronting wetlands at the shore landward of narrow beaches.

This coastal belt lies on the border between Tundra (ET) and Subarctic (Dfc) climates, with tundra immediately along the coast merging in a short distance inland to subarctic (Peel, Finlayson, and McMahon, 2007). Arctic tundra occurs in the far Northern Hemisphere, north of the taiga belt, and the treeless tundra is prominent in the north of Russia, generally above the Arctic Circle. The word “tundra” usually refers only to the areas where the subsoil is permafrost or permanently frozen soil. Permafrost tundra includes vast areas of northern Russia. The Laptev Sea Large Marine Ecosystem (LME57) is partly bordered by the Russian Federation. Adjacent terrestrial ecological systems include the Palearctic Realm, Tundra Biome, and the Taimyr-Central Siberian Tundra Ecoregion (ER781).

Lying on the northern fringe of the Anabar Massiff (Anabar Shield–Siberian Carton), northern central Siberia, the bedrock and surficial geology of the region contains Pliocene to Holocene (5.3–0.0 Ma) (5.333–0.0117 Ma) sediments that are dominantly comprised by sandstones, siltstones, and shales with intercalated minor beds of coal and plant fossils (e.g., Harrison et al., 2011; Meyer and Freeman, 2006). This Russian Federation coastal belt is characterized by tundra topography that includes soil above the permafrost or bedrock, referred to as the active layer, which becomes saturated like a wet sponge to produce, under polar climates, wetlands that are typical of much wetter environments. The freeze-thaw landforms that are characteristic of this tundra topography contain swamps, ponds, lakes, lagoons, bogs, marshes, and river and stream corridor wetlands.

Estonia Coastal Belt (Baltic Sea, Gulf of Finland Coast)

This satellite image was acquired on 27 April 2019, and located at 59°13′28″ N by 23°44′45″ E. The subpolar cross-shore transects on the northwest coast of Estonia extend about 3.5 km inland the southern margin of the Gulf of Finland, which merges with the Baltic Sea. The image lies about 3 km south of Keibu, situated in Lääne-Nigula Parish, Lääne County, in the region of Jõgevamaa. Lepaauk Lake, the large coastal lake on the western margin of the image, is one of many coastal lakes and lagoons in the region (Kose, 2012). The lagoons and lakes belong to different succession stages of the halotrophic lake type and most of them are very shallow, less than 1 m, while the depth of some others reaches 2–3 m. The bottom is usually sandy or clayey, often silted, seldom with a thick mud layer. Lacustrine sediments are mixed with beach, dune, flat, and beach ridge deposits, as shown in ground-penetrating radar studies (e.g., Vilumaa et al., 2013). The water is unstratified and rich in oxygen, mostly transparent to the bottom (Timm, 2007).

The climate surrounding the Baltic Sea Large Marine Ecosystem (LME23) and more localized environments of this coastal belt is classified as Dfb (Warm-Summer Humid Continental Climate) in the Köppen-Geiger system. The terrestrial environment classifies out as Palearctic Realm, Temperate Broadleaf and Mixed Forests Biome, and Sarmatic Mixed Forests Ecoregion (ER679). This ecoregion is situated in Europe between boreal forests/taiga in the north and the broadleaf belt in the south, and occupies about 846,100 km2 in southernmost Norway, southern Sweden (except southernmost), southwestern-most Finland, northern Lithuania, Latvia, Estonia, northern Belarus, and the central part of the European Russian Federation (Dinerstein et al., 2017).

The lithospheric crust in Estonia, part of the East European Craton, was formed in the early Paleoproterozoic nearly two billion years ago. Shallow marine geological environments now predominate in Estonia, producing extensive natural resources from organic matter, such as oil shale and phosphorite. The framework geology of this coastal belt is characterized by Middle Ordovician (485.4–458.4 Ma) limestones with minor inclusions of marl, shale/slate, and sandstone (Asch, 2003). The Mesozoic and much of the Cenozoic are not well-preserved in the rock record, although Pleistocene glaciations buried deep valleys in sediment, rechanneled streams, and left a landscape of extensive lakes, beach ridge straindplains, wetland marshes, and peat bogs (Puuar and Raukas, 1997).

United States Coastal Belt (State of Oregon, Pacific Coast)

This satellite image was acquired on 24 July 2019 and located at 44°44′47″ N by 124°03′26″ W along the central-western Oregon Pacific coast. The image centers on Otter Rock and lies about 7 km south of Depoe Bay and 10 km north of Newport. This middle latitude location includes the Devil's Punchbowl State Park, situated between Depoe Bay and Yaquina Head, Otter Rock. The Devils Punch Bowl area is known for creating some of the Oregon coast's most dramatic headlands. The eroded yellow sandstone bowl itself was formed by the convergence and collapse of two caves amid the relentless beating of the ocean's powerful waves. Otter Rock is part of the Otter Rock Marine Reserve, where specific prohibitions against fishing and the harvesting of fish, invertebrates, and seaweed were put into effect.

This coastal belt, which lies in a Csb (Mediterranean–Cool Dry-Summer Climate) Köppen-Geiger climatic zone, is bounded seaward by the California Current Large Marine Ecosystem (LME3) and landward by the terrestrial Nearctic Realm, Temperate Coniferous Forest Biome, and Central Pacific Northwest Coastal Forests Ecoregion (ER351). The forests of the Central Pacific Coast are among the most productive in the world, characterized by large trees, substantial woody debris, luxuriant growths of mosses and lichens on trees, and an abundance of ferns and herbs on the forest floor. The major forest complex consists of Douglas-fir (Pseudotsuga menziesii) and western hemlock (Tsuga heterophylla), encompassing seral forests dominated by Douglas-fir and massive old-growth forests of fir, hemlock, western red cedar (Thuja plicata), and other species. These forests occur from sea level up to elevations of 700–1000 m in the coastal range. This forest type occupies a wide range of environments with variable composition and structure and includes such other species as grand fir (Abies grandis), Sitka spruce (Picea sitchensis), and western white pine (Pinus monticola) (Franklin and Dyrness, 1988).

The bedrock geology of this coastal belt is partly composed of Miocene (23.03–5.333 Ma) sedimentary units that are dominated by sandstone, conglomerate, and siltstone (Peters, Husson, and Czaplewski, 2018). The sandstones are thick to thin-bedded while the conglomerates contain abundant clasts of pumice and dacitic volcanic rocks. The tuffaceous siltstones may be of deltaic origin (Miller, Raines, and Connors, 2002; Weaver et al., 1944). The headlands and promontories are mainly composed of igneous Columbia Basaltic sequences. These extrusive lavas flowed westward through a proto Columbia River Gorge millions of years ago and then made southward turns to fill large open areas. With perhaps hundreds or thousands of these eruptions over millions of years, the lavas built up and created most of the headlands along the Oregon coast. Yaquina Head, for example, is a promontory that sticks out into the sea. The mechanism for the alternating soft and hard rock sequences along the Oregon coast is based on long-term erosional processes that removed soft sedimentary rocks and left harder and more resistant igneous rocks behind as capes, headlands, or promontories. The lavas, which initially formed as igneous fills in valleys and submarine canyons of terrestrial sedimentary terrains, were more resistant than the host soft sediments that were eventually eroded away, exposing the hard lava rock that is seen today on headlands as examples of inverted topography.

Australian Coastal Belt (Queensland, Coral Sea Coast)

This satellite image, acquired on 3 July 2019, and located at 25°02′95″ S by 152°31′22″ E, shows a coastal belt in the Bundaberg Region of Queensland. This Austral subtropical coast lies about 7 km south of the Coonarr Creek estuary and about 2 km north of the Theodolite Creek inlet and Woodgate Beach facing the South Pacific Ocean along the Coral Sea. This Kinkuna Section of Burrum Coast National Park, which protects the catchment of Theodolite Creek, is about 280 km north-northwest of Brisbane. Lying at an elevation of approximately 36 m above sea level, the Kinkuna wetlands lie landward of the coastal foredunes. This remarkably unspoiled stretch of coastline features a long, sandy beach backed by low sand dunes, tea-colored waterways, and a variety of coastal vegetation communities from wallum heath to sedgelands, tall upland forests, low stunted woodlands, wetland marshes and salinas, and paperbark swamps (Melaleuca quinquenervia (Cav.) S.T.Blake) (Burrum Coast National Park Management Staff, 2013; Griffith, Bale, and Adam, 2008).

This coastal belt is bordered seaward by the East-Central Australian Shelf Large Marine Ecosystem (LME41) and falls under a Cfa (Humid Subtropical) climate in the Köppen-Geiger classification system. The terrestrial ecosystems (realms, biomes, and ecoregions) are classified in the Australasia Realm, Temperate Broadleaf and Mixed Forests Biome, Eastern Australian Temperate Forests Ecoregion (ER168). The regional geology encompassing this Australian coastal belt is characterized by Quaternary (2.588–0 Ma) regolithic deposits composed of coastal sand dunes and calcareous and siliceous barrier beaches (Raymond et al., 2012). Also present are estuarine and deltaic deposits, shown by intermittent lagoonal features with silt and evaporite deposits that may contain older vegetated black soils. This area is backed to the east by the Burrum Coast National Park that overlies part of the Middle Eocene to Oligocene (47.8–23.03 Ma) Elliott Formation, which is composed of sedimentary siliciclastics such as quartzose to sublabile sandstone, conglomerate, siltstone, mudstone, and shale.

Republic of The Gambia Coastal Belt (Western Africa, Atlantic Coast)

This satellite image of a tropical coastal belt was acquired on 13 January 2020, and located at 13°14′47″ N by 15°46′55″ W. The cross-shore transects are extended about 1 km inland while the alongshore section of the coastal belt extends for about 6 km. The northern barrier spit is about 200 m wide (including the beach and wetland). The dry beach has a maximum width of about 100 m. The lagoon is about 2.8 km long and lies about 2.3 km west of Kachuma and 24 km southwest of the Gambia River estuary. This coastal belt contains a wide variety of habitats that include marine, estuarine, freshwater, upland scrub woodland, and dry woodland savannah.

This area is known for its ornithological importance, which is evident from more than 259 species of birds from 61 different families being recorded. The large diversity of birds results from the range of habitats present combined with location of the Tanji Bird Reserve on the tropical coast of Western Africa. For European migrants, the Tanji Bird Reserve is one of the first stops offshore and offers both a safe haven as well as good feeding opportunities. The offshore Bijol Islands are used as a roosting site by large numbers of gulls, terns, waders, and pelicans, and the shallow surrounding reef offers good feeding opportunities as well. The Bijols Islands, part of Tanji Karinti Birds Reserve, cover an area of about 612 km and are managed by the Department of Parks and Wildlife Management. The vegetation of the islets is salt tolerant and includes morning glory (members of the Convolvulaceae family) and Baobab and Casuarinas trees spreading all over the larger island providing the only ideal breeding site for sea birds in this region. The areas are low lying with maximum elevation of about 2 m above the mean water level.

The general background geology of this coastal belt is characterized by Tertiary (66–2.588 Ma) and Quaternary (2.588–0 Ma) sedimentary rocks (Schlüter, 2008; Thiéblemont, 2016). Geologic complexes of the Holocene Epoch are primarily alluvial deposits of coarse sand and silt along the Gambia River and coastal beach complexes of undivided sand and silt. In eastern sections of the country, the upland Quaternary formations, away from alluvial lowlands, are characterized lateritic, ironstone, gravel formations.

The climate of this coastal belt falls under the Aw (Tropical Savanna) umbrella in the Köppen-Geiger climate classification system. The Canary Current Large Marine Ecosystem (LME27) lies offshore while along- and onshore the terrestrial ecological systems fall into the Afrotropic Realm, Mangroves Biome, and Guinean Mangroves Ecoregion (ER113).

METHODS

The methodology of this study entailed the acquisition of satellite imagery from the Google Earth Pro platform by selecting one coastal belt example from polar, subpolar, middle latitude, subtropical, and tropical regions. The five satellite images were selected on the basis of their potential relevance to coastal classification, which was geared towards recognition of cross-shore eco-geomorphological features in undeveloped areas. That procedure obviated selection of commercialized coastal belts because it would have been difficult to interpret natural environments from anthropogenically-influenced areas. Because so many of the world's coastal environments are developed or altered, study sites were necessarily confined to remote regions or protected areas, such as parks or reserves, that exist without close proximity to urban centers. The selection of image sites was also constrained by acquisition of scenes that were devoid of cloud cover, dark screening or haze cover, presence of blue-wash air brushing in the offshore zone, and poor image matching where color tones were not well calibrated between satellite images (e.g., the result of which produces line contrasts of varying intensity). Accommodating these procedures, it was possible to download high-quality imagery from Google Earth Pro that provided good examples for desired seascape interpretation.

The downloaded satellite image scenes were of different scales depending on how the natural environments that were depicted. Because viewing coastal environments is scale dependent, each image was adjusted to an appropriate scale for observation, interpretation, and classification. Map scales and north arrows are provided in each image for orientation and perceptions of linear distances. Due to the nature of coastal morphodynamic features and ecological environments, the images are mostly larger scales and cover relatively small alongshore distances; viz. 14.2 km for the Russian Federation Coastal Belt (Figure 1a), 4.7 km for the Estonia Coastal Belt (Figure 2a), 3.6 km for the United States Coastal Belt (Figure 3a), 3.7 km for the Australian Coastal Belt (Figure 4a), and 3.9 km for the Republic of The Gambia Coastal Belt (Figure 5a). These alongshore distances average about 3.9 km (the exception being from the Russian Federation) showing the scale of observation that is adequate for this proposed cross-shore, and ultimately alongshore, classification.

Figure 1

(a; opposing page) This satellite image (acquisition date: 13 December 2015; grid location: 73°41′34″ N by 115°27′28 ″ E) was acquired from Google Earth Pro as an example of a polar coastal belt from the Russian Federation (Laptev Sea, Gulf of Anabar coast). (b) The coastal belt between A-A′ and B-B′ is approximately 13.6 km long and divided into 12 cross-shore transects (separated by thin red lines; Table 3), with the Large Marine Ecosystem (LME) designation in blue print and the terrestrial Ecoregion (ER) designation in brown print. Cross-shore transect catenary sequences composed of archetypes and sub archetypes are indicated in yellow print with the alphanumerical code approximating the location of the eco-geomorphological feature identified (refer to Table 1 for code definitions). An example of an individual alongshore domain composed of an archetypical catena sequence is shown in orange print with a double-ended arrow signaling the width of the alongshore codified swath. The Concised Alongshore Super Domain for the entire coastal belt is shown in green print and is formulated to be Barrier-Beach-Delta-Flat-Lagoon-Wetland-Upland (Ba-Be-De-F-L-W-U).

Figure 1

(a; opposing page) This satellite image (acquisition date: 13 December 2015; grid location: 73°41′34″ N by 115°27′28 ″ E) was acquired from Google Earth Pro as an example of a polar coastal belt from the Russian Federation (Laptev Sea, Gulf of Anabar coast). (b) The coastal belt between A-A′ and B-B′ is approximately 13.6 km long and divided into 12 cross-shore transects (separated by thin red lines; Table 3), with the Large Marine Ecosystem (LME) designation in blue print and the terrestrial Ecoregion (ER) designation in brown print. Cross-shore transect catenary sequences composed of archetypes and sub archetypes are indicated in yellow print with the alphanumerical code approximating the location of the eco-geomorphological feature identified (refer to Table 1 for code definitions). An example of an individual alongshore domain composed of an archetypical catena sequence is shown in orange print with a double-ended arrow signaling the width of the alongshore codified swath. The Concised Alongshore Super Domain for the entire coastal belt is shown in green print and is formulated to be Barrier-Beach-Delta-Flat-Lagoon-Wetland-Upland (Ba-Be-De-F-L-W-U).

Figure 1

Continued

Figure 1

Continued

Table 3

Cross-shore transect and alongshore domain code sequencing for the polar coastal belt in the Russian Federation. Each of the 12 cross-shore transects are listed in terms of the Coastal Belt Linked Classification (CBLC; Finkl and Makowski, 2020d) and the Biophysical Cross-shore Classification System (BCCS; Finkl and Makowski, 2020a). Each individual alongshore domain is listed in terms of their respective Dominant Catenary Sequence (DCS). The Conflated Alongshore CBLC Domain, the Concised Alongshore BCCS Domain, and the Concised Alongshore Super Domain are also listed for this coastal belt.

Cross-shore transect and alongshore domain code sequencing for the polar coastal belt in the Russian Federation. Each of the 12 cross-shore transects are listed in terms of the Coastal Belt Linked Classification (CBLC; Finkl and Makowski, 2020d) and the Biophysical Cross-shore Classification System (BCCS; Finkl and Makowski, 2020a). Each individual alongshore domain is listed in terms of their respective Dominant Catenary Sequence (DCS). The Conflated Alongshore CBLC Domain, the Concised Alongshore BCCS Domain, and the Concised Alongshore Super Domain are also listed for this coastal belt.
Cross-shore transect and alongshore domain code sequencing for the polar coastal belt in the Russian Federation. Each of the 12 cross-shore transects are listed in terms of the Coastal Belt Linked Classification (CBLC; Finkl and Makowski, 2020d) and the Biophysical Cross-shore Classification System (BCCS; Finkl and Makowski, 2020a). Each individual alongshore domain is listed in terms of their respective Dominant Catenary Sequence (DCS). The Conflated Alongshore CBLC Domain, the Concised Alongshore BCCS Domain, and the Concised Alongshore Super Domain are also listed for this coastal belt.
Figure 2

(a; opposing page) This satellite image (acquisition date: 27 April 2019; grid location: 59°13′28″ N by 23°44′45″ E) was acquired from Google Earth Pro as an example of a subpolar coastal belt from Estonia (Baltic Sea, Gulf of Finland coast). (b) The coastal belt between A-A′ and B-B′ is approximately 2.7 km long and divided into five (5) cross-shore transects (separated by thin red lines; Table 4), with the Large Marine Ecosystem (LME) designation in blue print and the terrestrial Ecoregion (ER) designation in brown print. Cross-shore transect catenary sequences composed of archetypes and sub archetypes are indicated in yellow print with the alphanumerical code approximating the location of the eco-geomorphological feature identified (refer to Table 1 for code definitions). An example of an individual alongshore domain composed of an archetypical catena sequence is shown in orange print with a double-ended arrow signaling the width of the alongshore codified swath. The Concised Alongshore Super Domain for the entire coastal belt is shown in green print and is formulated to be Barrier-Beach-Dune-Beach Ridge-Upland-Wetland-Beach Ridge (Ba-Be-Du-Br-U-W-Br).

Figure 2

(a; opposing page) This satellite image (acquisition date: 27 April 2019; grid location: 59°13′28″ N by 23°44′45″ E) was acquired from Google Earth Pro as an example of a subpolar coastal belt from Estonia (Baltic Sea, Gulf of Finland coast). (b) The coastal belt between A-A′ and B-B′ is approximately 2.7 km long and divided into five (5) cross-shore transects (separated by thin red lines; Table 4), with the Large Marine Ecosystem (LME) designation in blue print and the terrestrial Ecoregion (ER) designation in brown print. Cross-shore transect catenary sequences composed of archetypes and sub archetypes are indicated in yellow print with the alphanumerical code approximating the location of the eco-geomorphological feature identified (refer to Table 1 for code definitions). An example of an individual alongshore domain composed of an archetypical catena sequence is shown in orange print with a double-ended arrow signaling the width of the alongshore codified swath. The Concised Alongshore Super Domain for the entire coastal belt is shown in green print and is formulated to be Barrier-Beach-Dune-Beach Ridge-Upland-Wetland-Beach Ridge (Ba-Be-Du-Br-U-W-Br).

Figure 2

Continued

Figure 2

Continued

Table 4

Cross-shore transect and alongshore domain code sequencing for the subpolar coastal belt in Estonia. Each of the five (5) cross-shore transects are listed in terms of the Coastal Belt Linked Classification (CBLC; Finkl and Makowski, 2020d) and the Biophysical Cross-shore Classification System (BCCS; Finkl and Makowski, 2020a). Each individual alongshore domain is listed in terms of their respective Dominant Catenary Sequence (DCS). The Conflated Alongshore CBLC Domain, the Concised Alongshore BCCS Domain, and the Concised Alongshore Super Domain are also listed for this coastal belt.

Cross-shore transect and alongshore domain code sequencing for the subpolar coastal belt in Estonia. Each of the five (5) cross-shore transects are listed in terms of the Coastal Belt Linked Classification (CBLC; Finkl and Makowski, 2020d) and the Biophysical Cross-shore Classification System (BCCS; Finkl and Makowski, 2020a). Each individual alongshore domain is listed in terms of their respective Dominant Catenary Sequence (DCS). The Conflated Alongshore CBLC Domain, the Concised Alongshore BCCS Domain, and the Concised Alongshore Super Domain are also listed for this coastal belt.
Cross-shore transect and alongshore domain code sequencing for the subpolar coastal belt in Estonia. Each of the five (5) cross-shore transects are listed in terms of the Coastal Belt Linked Classification (CBLC; Finkl and Makowski, 2020d) and the Biophysical Cross-shore Classification System (BCCS; Finkl and Makowski, 2020a). Each individual alongshore domain is listed in terms of their respective Dominant Catenary Sequence (DCS). The Conflated Alongshore CBLC Domain, the Concised Alongshore BCCS Domain, and the Concised Alongshore Super Domain are also listed for this coastal belt.
Figure 3

(a; opposing page) This satellite image (acquisition date: 24 July 2019; grid location: 44°44′47″ N by 124°03′26″ W) was acquired from Google Earth Pro as an example of a middle latitude coastal belt from the United States (State of Oregon, Pacific coast). (b) The coastal belt between A-A′ and B-B′ is approximately 5.6 km long and divided into 11 cross-shore transects (separated by thin red lines; Table 5), with the Large Marine Ecosystem (LME) designation in blue print and the terrestrial Ecoregion (ER) designation in brown print. Cross-shore transect catenary sequences composed of archetypes and sub archetypes are indicated in yellow print with the alphanumerical code approximating the location of the eco-geomorphological feature identified (refer to Table 1 for code definitions). An example of an individual alongshore domain composed of an archetypical catena sequence is shown in orange print with a double-ended arrow signaling the width of the alongshore codified swath. The Concised Alongshore Super Domain for the entire coastal belt is shown in green print and is formulated to be Beach-Rock-Cliff-Upland (Be-R-Cl-U).

Figure 3

(a; opposing page) This satellite image (acquisition date: 24 July 2019; grid location: 44°44′47″ N by 124°03′26″ W) was acquired from Google Earth Pro as an example of a middle latitude coastal belt from the United States (State of Oregon, Pacific coast). (b) The coastal belt between A-A′ and B-B′ is approximately 5.6 km long and divided into 11 cross-shore transects (separated by thin red lines; Table 5), with the Large Marine Ecosystem (LME) designation in blue print and the terrestrial Ecoregion (ER) designation in brown print. Cross-shore transect catenary sequences composed of archetypes and sub archetypes are indicated in yellow print with the alphanumerical code approximating the location of the eco-geomorphological feature identified (refer to Table 1 for code definitions). An example of an individual alongshore domain composed of an archetypical catena sequence is shown in orange print with a double-ended arrow signaling the width of the alongshore codified swath. The Concised Alongshore Super Domain for the entire coastal belt is shown in green print and is formulated to be Beach-Rock-Cliff-Upland (Be-R-Cl-U).

Figure 3

Continued

Figure 3

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Table 5

Cross-shore transect and alongshore domain code sequencing for the middle latitude coastal belt in the United States. Each of the 11 cross-shore transects are listed in terms of the Coastal Belt Linked Classification (CBLC; Finkl and Makowski, 2020d) and the Biophysical Cross-shore Classification System (BCCS; Finkl and Makowski, 2020a). Each individual alongshore domain is listed in terms of their respective Dominant Catenary Sequence (DCS). The Conflated Alongshore CBLC Domain, the Concised Alongshore BCCS Domain, and the Concised Alongshore Super Domain are also listed for this coastal belt.

Cross-shore transect and alongshore domain code sequencing for the middle latitude coastal belt in the United States. Each of the 11 cross-shore transects are listed in terms of the Coastal Belt Linked Classification (CBLC; Finkl and Makowski, 2020d) and the Biophysical Cross-shore Classification System (BCCS; Finkl and Makowski, 2020a). Each individual alongshore domain is listed in terms of their respective Dominant Catenary Sequence (DCS). The Conflated Alongshore CBLC Domain, the Concised Alongshore BCCS Domain, and the Concised Alongshore Super Domain are also listed for this coastal belt.
Cross-shore transect and alongshore domain code sequencing for the middle latitude coastal belt in the United States. Each of the 11 cross-shore transects are listed in terms of the Coastal Belt Linked Classification (CBLC; Finkl and Makowski, 2020d) and the Biophysical Cross-shore Classification System (BCCS; Finkl and Makowski, 2020a). Each individual alongshore domain is listed in terms of their respective Dominant Catenary Sequence (DCS). The Conflated Alongshore CBLC Domain, the Concised Alongshore BCCS Domain, and the Concised Alongshore Super Domain are also listed for this coastal belt.
Figure 4

(a; opposing page) This satellite image (acquisition date: 3 July 2019; grid location: 25°02′95″ S by 152°31′22″ E) was acquired from Google Earth Pro as an example of a subtropical coastal belt from Australia (Queensland, Coral Sea coast). (b) The coastal belt between A-A′ and B-B′ is approximately 3.6 km long and divided into three (3) cross-shore transects (separated by thin red lines), with the Large Marine Ecosystem (LME) designation in blue print and the terrestrial Ecoregion (ER) designation in brown print. Cross-shore transect catenary sequences composed of archetypes and sub archetypes are indicated in yellow print with the alphanumerical code approximating the location of the eco-geomorphological feature identified (refer to Table 1 for code definitions). An example of an individual alongshore domain composed of an archetypical catena sequence is shown in orange print with a double-ended arrow signaling the width of the alongshore codified swath. The Concised Alongshore Super Domain for the entire coastal belt is shown in green print and is formulated to be Barrier-Beach-Dune-Lagoon-Upland-Wetland (Ba-Be-Du-L-U-W).

Figure 4

(a; opposing page) This satellite image (acquisition date: 3 July 2019; grid location: 25°02′95″ S by 152°31′22″ E) was acquired from Google Earth Pro as an example of a subtropical coastal belt from Australia (Queensland, Coral Sea coast). (b) The coastal belt between A-A′ and B-B′ is approximately 3.6 km long and divided into three (3) cross-shore transects (separated by thin red lines), with the Large Marine Ecosystem (LME) designation in blue print and the terrestrial Ecoregion (ER) designation in brown print. Cross-shore transect catenary sequences composed of archetypes and sub archetypes are indicated in yellow print with the alphanumerical code approximating the location of the eco-geomorphological feature identified (refer to Table 1 for code definitions). An example of an individual alongshore domain composed of an archetypical catena sequence is shown in orange print with a double-ended arrow signaling the width of the alongshore codified swath. The Concised Alongshore Super Domain for the entire coastal belt is shown in green print and is formulated to be Barrier-Beach-Dune-Lagoon-Upland-Wetland (Ba-Be-Du-L-U-W).

Figure 4

Continued

Figure 4

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Figure 5

(a; opposing page) This satellite image (acquisition date: 13 January 2020; grid location: 13°14′47″ N by 15°46′55″ W) was acquired from Google Earth Pro as an example of a tropical coastal belt from the Republic of The Gambia (Western Africa, Atlantic coast). (b) The coastal belt between A-A′ and B-B′ is approximately 6.0 km long and divided into six (6) cross-shore transects (separated by thin red lines), with the Large Marine Ecosystem (LME) designation in blue print and the terrestrial Ecoregion (ER) designation in brown print. Cross-shore transect catenary sequences composed of archetypes and sub archetypes are indicated in yellow print with the alphanumerical code approximating the location of the eco-geomorphological feature identified (refer to Table 1 for code definitions). An example of an individual alongshore domain composed of an archetypical catena sequence is shown in orange print with a double-ended arrow signaling the width of the alongshore codified swath. The Concised Alongshore Super Domain for the entire coastal belt is shown in green print and is formulated to be Barrier-Beach-Dune-Lagoon-Wetland-Upland (Ba-Be-Du-L-W-U).

Figure 5

(a; opposing page) This satellite image (acquisition date: 13 January 2020; grid location: 13°14′47″ N by 15°46′55″ W) was acquired from Google Earth Pro as an example of a tropical coastal belt from the Republic of The Gambia (Western Africa, Atlantic coast). (b) The coastal belt between A-A′ and B-B′ is approximately 6.0 km long and divided into six (6) cross-shore transects (separated by thin red lines), with the Large Marine Ecosystem (LME) designation in blue print and the terrestrial Ecoregion (ER) designation in brown print. Cross-shore transect catenary sequences composed of archetypes and sub archetypes are indicated in yellow print with the alphanumerical code approximating the location of the eco-geomorphological feature identified (refer to Table 1 for code definitions). An example of an individual alongshore domain composed of an archetypical catena sequence is shown in orange print with a double-ended arrow signaling the width of the alongshore codified swath. The Concised Alongshore Super Domain for the entire coastal belt is shown in green print and is formulated to be Barrier-Beach-Dune-Lagoon-Wetland-Upland (Ba-Be-Du-L-W-U).

Figure 5

Continued

Figure 5

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Initially, each satellite scene was visually scanned in order to determine the types of coastal features that could be assigned units following the BCCS (Biophysical Cross-shore Classification System) (Finkl and Makowski, 2020a) and the CBLC (Coastal Belt Linked Classification System) (Finkl and Makowski 2020d), the latter scheme incorporating or subsuming the former by adding the LME (Large Marine Ecosystem) and ER (Ecoregion) to the BCCS code sequence. Because the LME and ER are geographically large units, they were appropriately applied to whole satellite scenes and were simply notated once for offshore marine and terrestrial inland areas.

Cognitive inspection of each image provided the basis for interpretation of sequential cross-shore transects from unfixed points offshore to variable distances inland. Most cross-shore transects extended between 1 to 3 km inland, except for the Russian Federation Coastal Belt where some transects extended up to 8 km inland. These transect lengths were determined by the imagery and were sufficient to determine primary cross-shore eco-geomorphological catena sequences. Each satellite image was thus annotated with numerous cross-shore transects (ranging from 3 to 12 transects) which were then separated from each other by red-colored shore-normal boundary lines that extended from the sea to arbitrary cartographic inland limits. Completion of the series of cross-shore transects on each image provided an impression of shore-normal transectal variation, while at the same time providing a basis for discernment of intra- and intertransect variation.

The next procedure was to ascertain the manual correlatability between transects so that similarities and differences could be highlighted and eventually codified in terms of an alongshore classification sequence. This process is similar to conducting a manual correlation of well logs, via processes that are referred to as borehole, well, or downhole logging, where each borehole contains an abbreviated description of the various parameters of stratigraphic layers from the ground surface downwards (e.g., Keyes, 1996; Wonik and Olea, 2007). Well log correlation, whether manual or automatic, involves the identification and connection of equivalent patterns and/or values between log curves of adjacent wells. In this manner, it was possible to analogously summarize cross-shore variability in terms of alongshore variants so that each shore-parallel unit had extension inland. Annotation of the satellite images thus showed both cross-shore variations via transects as well as alongshore differences expressed by the swath width of transects. These new alongshore designations that contained inland extent are referred to as domains because they reflected both alongshore distance as well as inland extent of the eco-geomorphological features. Each satellite image contained one example of a summary alongshore domain that was identified by a definable width of cross-shore archetypical sequences or catenas. By repeating this process for successively adjacent transects, a shore-parallel classification was obtained where each alongshore domain was comprised by a sequence of archetypes, as described by Finkl and Makowski (2020a), in the BCCS. Addition of the LME and ER to the cross-shore BCCS catenas completes the CBLC codification, as per instructions by Finkl and Makowski (2020d).

All of the satellite images contain one notated cross-shore transect and one notated domain as an example of how the procedure proceeds. This example domain represents the base or essential cross-shore sequence of eco-geomorphological features when concised down to the minimum catenal sequence for brevity. When completeness or complexity of description is desired, the sequencing of archetypes and sub archetypes can be continued inland, but this procedure was omitted in these examples for clarity (i.e. not overburdening the satellite images with numerous annotations). Archetypical catenas for all transects in each of the five images are, however, supplied in Tables 37. By following the cross-shore sequencing process it will be discerned that the base units simply tend to repeat making longer sequences. In the interest of brevity and because the cross-shore units were going to be transfigured into shore-parallel units, only the basic catenary sequences were provided for illustrative purposes. The demarcation of transect boundaries is shown by red-colored shore-normal lines in each of the satellite images that in effect presents each domain as an alongshore montage of cross-shore catenas.

Table 6

Cross-shore transect and alongshore domain code sequencing for the subtropical coastal belt in Australia. Each of the three (3) cross-shore transects are listed in terms of the Coastal Belt Linked Classification (CBLC; Finkl and Makowski, 2020d) and the Biophysical Cross-shore Classification System (BCCS; Finkl and Makowski, 2020a). Each individual alongshore domain is listed in terms of their respective Dominant Catenary Sequence (DCS). The Conflated Alongshore CBLC Domain, the Concised Alongshore BCCS Domain, and the Concised Alongshore Super Domain are also listed for this coastal belt.

Cross-shore transect and alongshore domain code sequencing for the subtropical coastal belt in Australia. Each of the three (3) cross-shore transects are listed in terms of the Coastal Belt Linked Classification (CBLC; Finkl and Makowski, 2020d) and the Biophysical Cross-shore Classification System (BCCS; Finkl and Makowski, 2020a). Each individual alongshore domain is listed in terms of their respective Dominant Catenary Sequence (DCS). The Conflated Alongshore CBLC Domain, the Concised Alongshore BCCS Domain, and the Concised Alongshore Super Domain are also listed for this coastal belt.
Cross-shore transect and alongshore domain code sequencing for the subtropical coastal belt in Australia. Each of the three (3) cross-shore transects are listed in terms of the Coastal Belt Linked Classification (CBLC; Finkl and Makowski, 2020d) and the Biophysical Cross-shore Classification System (BCCS; Finkl and Makowski, 2020a). Each individual alongshore domain is listed in terms of their respective Dominant Catenary Sequence (DCS). The Conflated Alongshore CBLC Domain, the Concised Alongshore BCCS Domain, and the Concised Alongshore Super Domain are also listed for this coastal belt.
Table 7

Cross-shore transect and alongshore domain code sequencing for the tropical coastal belt in the Republic of The Gambia. Each of the six (6) cross-shore transects are listed in terms of the Coastal Belt Linked Classification (CBLC; Finkl and Makowski, 2020d) and the Biophysical Cross-shore Classification System (BCCS; Finkl and Makowski, 2020a). Each individual alongshore domain is listed in terms of their respective Dominant Catenary Sequence (DCS). The Conflated Alongshore CBLC Domain, the Concised Alongshore BCCS Domain, and the Concised Alongshore Super Domain are also listed for this coastal belt.

Cross-shore transect and alongshore domain code sequencing for the tropical coastal belt in the Republic of The Gambia. Each of the six (6) cross-shore transects are listed in terms of the Coastal Belt Linked Classification (CBLC; Finkl and Makowski, 2020d) and the Biophysical Cross-shore Classification System (BCCS; Finkl and Makowski, 2020a). Each individual alongshore domain is listed in terms of their respective Dominant Catenary Sequence (DCS). The Conflated Alongshore CBLC Domain, the Concised Alongshore BCCS Domain, and the Concised Alongshore Super Domain are also listed for this coastal belt.
Cross-shore transect and alongshore domain code sequencing for the tropical coastal belt in the Republic of The Gambia. Each of the six (6) cross-shore transects are listed in terms of the Coastal Belt Linked Classification (CBLC; Finkl and Makowski, 2020d) and the Biophysical Cross-shore Classification System (BCCS; Finkl and Makowski, 2020a). Each individual alongshore domain is listed in terms of their respective Dominant Catenary Sequence (DCS). The Conflated Alongshore CBLC Domain, the Concised Alongshore BCCS Domain, and the Concised Alongshore Super Domain are also listed for this coastal belt.

The penultimate step in this methodological approach involves the collection or assimilation of domains that make up the coastal belt under investigation. Application of this process produced between 3 and 12 domains per coastal belt in the examples provided here, one domain per cross-shore transect, depending on the complexity of the eco-geomorphological setup. An ultimate goal of this procedure is to then parse the domain codifications into a single master, composite, or super domain for the entire coastal belt, or logical sections of it. This complete progression of formulating a Concised Alongshore Super Domain for each coastal belt can be seen in Tables 37.

RESULTS

The results of this investigation are presented in both graphic (Figures 15) and tabular (Tables 37) formats, the former pictorially displaying the mapping procedures and the latter summarizing the cross-shore sequencing of archetypes and sub archetypes, and the development of Concised Alongshore Super Domains for each coastal belt. Used together, the annotated satellite images and tables provide an overview of how cross-shore catenas can be transitioned into alongshore classificatory sequences (i.e. domains). A summary of the codifications used for archetypes and sub archetypes in the BCCS and CBLC is presented in Table 1, which correspond to the annotated satellite images, tables, and text descriptions used in this study. The codes in Table 1 are largely those provided previously by Finkl and Makowski (2020a) as comprehensive suggestions, although all of the units identified there are not used in this study's examples. For ease of image use and visual cross referencing, the annotated satellite images are paired with corresponding unannotated images as a cross-page figure expansion (this can be seen in Figures 1a, 2a, 3a, 4a, and 5a). Readers are encouraged to compare the images in each diptych (two-image cross-page expansion) to better appreciate and evaluate the results of image interpretation. Table 2provides the general geographical location summaries of all five satellite images used in this study, gives the latitudinal zone and grid coordinates of each image, the Köppen-Geiger climate classification as well as the Large Marine Ecosystem (LME) and Ecoregion (ER), and finally the Concised Alongshore Super Domains (i.e. dominant catenary sequences) for each coastal belt.

One example of an individual domain classification is provided in each image as an illustration of how the process should proceed to develop a concised alongshore classification of the coastal belt in the form of a super domain. Although the process can be generalized or refined as much as desired, the examples provided here are the result of scale-dependent interpretations that arrive at a domain that is characterized by a dominant archetype catena. Tables 3 through 7 provide summary details showing how the ultimate result is obtained in the form of a Concised Alongshore Super Domain by first compiling the Conflated Alongshore CBLC Domain (for the entire coastal belt between A-A' to B-B'). The results of this effort were obtained by listing all of the CBLC codification sequences in the coastal belt, as described in Finkl and Makowski (2020d). All of the archetypes and sub archetypes in the coastal belt were then merged into a single master sequence that is referred to as a Conflated Alongshore CBLC Domain. This conflation assembled the diversity of archetypes and sub archetypes into a single catenary unit without having to repeat unnecessary duplicate units. In a similar manner, the Concised Alongshore BCCS Domain was assembled from the list of BCCS units into one average catenary association without inclusion of duplicate archetypes and sub archetypes. The results of this procedure are presented as a single code for the entire coastal belt from A-A' to B-B' that can be used in codified form or converted to verbiage and linked to a mapping unit if so desired. The Concised Alongshore BCCS Domain is similar to the Conflated Alongshore CBLC Domain but lacks the LME and ER designations. Both codifications can be used in conjunction or separately to display the results of interpreting shore-normal eco-geomorphological catenary sequences. The penultimate step occurs with the shore-parallel configuration numbers and sub archetypes are removed to create a list of individual alongshore domains for the entire coastal belt (from A-A' to B-B') in form of dominant catenary sequences (DCS). Lastly, the individual domains are merged into one master representative sequence as the Concised Alongshore Super Domain of the entire coastal belt.

Russian Federation Coastal Belt (Laptev Sea, Gulf of Anabar Coast)

The results of image interpretation along the shore of the Russian Federation coastal belt are shown in the diptych for Figure 1, where Figure 1a is the unannotated image and Figure 1b is the annotated image. Interpretation of the polar image follows basic cognitive procedures, as described previously by Finkl and Makowski (2020a,d) for the application of the Biophysical Cross-shore Classification System (BCCS) and Coastal Belt Linked Classification System (CBLC). The results of the cross-shore cognitive interpretations are shown in the 12 delineated transects where the codifications are derived from Table 1. The widths of the transects (i.e. domains) were determined from both alongshore and cross-shore inspection and interpretation of the eco-geomorphological features of the coastal belt. This dual-phase cognition resulted in the placement of the transect boundaries. The codifications comprising each transect are summarized in Table 3 and lists both the CBLC and the BCCS sequences. These summary lists of cross-shore codifications are respectively reduced to a Conflated Alongshore CBLC Domain and a Concised Alongshore BCCS Domain. Conflation of the CBLC catenas resulted in a single summary cross-shore sequence that contained all of the eco-geomorphological features, including the LME and ER. The Russian Federation coastal belt example was thus built up to the following full CBLC conflated alongshore domain sequence based on the combination of the 12 transects to produce the following catenary sequence for the entire swath of the coastal belt: (LME57)2,7BabiBesiDeFsaLopWswUtu(ER781). The Concised Alongshore BCCS Domain, which does not include the LME or ER, was then reduced down to: 2,7BabiBesiDeFsaLopWswUtu. And finally, the Concised Alongshore Super Domain, which is devoid of shore-parallel configuration numbers or sub archetypes, represents the overall alongshore dominant catenary sequence for the entire coastal belt with: Ba-Be-De-F-L-W-U.

Of particular note is the alongshore domain shown in Figure 1b differs from the other 11 transect catenas by lacking inclusion of the barrier archetype. Being located in the actual river delta, the archetypical catenary sequence renders De-F-L-W-U. This shows how the process produces an alongshore domain for each cross-shore transect. In this particular case, the adjacent transect to the west would have a Dominant Catenary Sequence (DCS) of Ba-Be-W-U and the adjacent transect to the east would have a dominant catenary sequence of Ba-Be-F-W-U. Consideration of the entire coastal belt from boundaries A-A' to B-B' results in this master archetypical catenary sequence (i.e. Concised Alongshore Super Domain): Barrier-Beach-Delta-Flat-Lagoon-Wetland-Upland (Ba-Be-De-F-L-W-U). Verbalization of the super domain catena expands to include a barrier beach coastal belt with deltas, tidal flats, and lagoons that grade inland to varied wetlands and uplands.

Estonia Coastal Belt (Baltic Sea, Gulf of Finland Coast)

The results of image interpretation along the shore of the Estonia coastal belt are shown in the diptych for Figure 2, where Figure 2a is the unannotated image and Figure 2b is the annotated image. The results of the cross-shore cognitive interpretations are shown in the five transects, where the codifications were derived from Table 1. The widths of the transects were determined from both alongshore and cross-shore covisual inspection and interpretation of the eco-geomorphological features of this subpolar coastal belt. This dual-phase cognition resulted in the placement of the transect boundaries that were based on marine and terrestrial biophysical systems. The codifications comprising each transect are summarized in Table 4 and lists both the Coastal Belt Linked Classification System (CBLC) and the Biophysical Cross-shore Classification System (BCCS) sequences. The Conflated Alongshore CBLC Domain (for the entire coastal belt between A-A' to B-B'), which can be used as a descriptor for the 4.7-km long coastal belt segment, was built up from summations of the five transects into one conflated sequence: (LME23)7BabiBesiDu BrspUsrWmrBrsp(ER679). The Concised Alongshore BCCS Domain (for the entire coastal belt A-A' to B-B'), which is has omitted the Large Marine Ecosystem (LME) and Ecoregion (ER) designations, is parsed down to: 7BabiBesiDuBrspUsr WmrBrsp. Lastly, the Concised Alongshore Super Domain, which is without shore-parallel configuration numbers or sub archetypes, represents the overall alongshore Dominant Catenary Sequence (DCS) for the entire coastal belt with: Barrier-Beach-Dune-Beach Ridge-Upland-Wetland-Beach Ridge (BaBe-Du-Br-U-W-Br).

Verbal translation of the super domain code sequence for the whole coastal belt refers to a barrier beach-dune complex that is backed by reoccurring beach ridges and interspersed with uplands and wetlands. The beach ridge plains extend at least 3.5 km inland forming a broad concave seaward arc that is broadly curvilinear overall but containing short interspersed straight segments. The swaths of the five domains vary in length respectively from north to south as follows: 748, 604, 466, 743, and 203 m, which make up the 2.7-km long coastal belt shown in the satellite image (Figure 2b). The fourth alongshore domain, counted from the northern A-A' boundary southwards, identifies the summary Barrier-Beach-Dune-Beach Ridge (Ba-Be-Du-Br) archetypical sequence for that individual coastal segment.

United States Coastal Belt (State of Oregon, Pacific Coast)

The results of image interpretation along the middle latitude United States coastal belt are shown in the diptych for Figure 3, where Figure 3a is the unannotated image and Figure 3b is the annotated image. Cross-shore cognitive interpretations are shown in the eleven transects where the sequences are summarized in Table 5. The widest cross-shore transects that translate to alongshore domains include the first segment on the northern margin of the image below the northern A-A' boundary and the last segment at the bottom of the image above the southern B-B' boundary. The northern domain at the top of the image spans an alongshore distance of about 1 km, while the southern domain at the bottom of the image is about 1.8 km long. The latter domain extends southwards another 1.6 km out of frame to the next inlet marking the length of this mainland beach. The remaining nine domains average about 350 m each in alongshore distance.

The longest domain in this image was selected as an example for both the cross-shore transect and alongshore domain interpretations. The cross-shore transect sequence was comprised of an alphanumeric code that contains three sub archetypes and shore-parallel configuration descriptors (2 and 7 for curved and straight coastal configurations, respectively): 2,7BesiClseUfo. The sub archetype codifications translate to the simple verbiage of a silica beach backed by sedimentary cliffs that are topped by forested uplands. The conflated 5.6 km alongshore Coastal Belt Linked Classification (CBLC) domain (for the entire coastal belt between A-A' to B-B') expands to include archetypes, sub archetypes, and shore-parallel configuration numbers from all of the 11 individual transects, plus the Large Marine Ecosystem (LME) and Ecoregion (ER) numbers, forming: (LME3)2,6,7Berp,siRpl,tsClig,seUfo(ER351). Considering the entire length of this coastal belt in the satellite image, the Conflated Alongshore CBLC Domain for the beach archetype includes ramparts and silica compositional sub archetypes. Rocky archetypes include platform and talus sub archetypes that lie seaward of beaches or cliffs. Because both basaltic headlands and sedimentary formations form cliffs, each is noted as a subscript to the cliff (Cl) designator as ig for igneous lithologies and se for sediments. The upland archetype is universally forested, except for some minor urbanization, and so the sub archetype is designated by the fo subscript for forest.

The Concised Alongshore BCCS Domain (for the entire coastal belt A-A' to B-B') simplifies the Conflated Alongshore CBLC Domain by removing the representation of the LME and ER: 2,6,7Berp,siRpl,tsClig,seUfo. Lastly, the Concised Alongshore Super Domain (for the entire coastal belt A-A' to B-B') is further simplified to the basic archetypical sequence of Beach-Rock-Cliff-Upland (Be-R-Cl-U), which does not include subscript designators for sub archetypes. The concised simplicity of the super domain provides an alongshore classification based on cross-shore transect interpretation.

Australian Coastal Belt (Queensland, Coral Sea Coast)

The results of image interpretation along the subtropical Australian coastal belt are shown in the diptych for Figure 4, where Figure 4a is the unannotated image and Figure 4b is the annotated image. The cross-shore cognitive interpretations are shown in the three transects and summarized in Table 6. The widest cross-shore transect, which was used as an example of an individual alongshore domain, was delineated by the middle segment in the central portion of the image between the northern A-A' boundary and the southern B-B' boundary.

The swath widths of the transects were determined from both alongshore and cross-shore covisual inspection and interpretation of the eco-geomorphological features. The three domains vary in alongshore length from north to south as follows: 857, 2200, and 570 m. They collectively make up the 3.6-km long coastal belt shown in the satellite image. This dual-phase cognition resulted in the placement of the transect boundaries that were based on marine and terrestrial ecological zones and systems. The codifications comprising each transect are summarized in Table 6 and lists both the Coastal Belt Linked Classification System (CBLC; individual cross-shore transects from A-A' to B-B') and the Biophysical Cross-shore Classification System (BCCS; individual cross-shore transects from A-A' to B-B') sequences. For example, the second alongshore domain, counted from the northern A-A' boundary southwards, identifies the summary Barrier-Beach-Dune-Lagoon-Upland-Wetland (Ba-Be-Du-L-U-W) archetypal sequence for the middle coastal segment.

The Conflated Alongshore CBLC Domain (for the entire coastal belt between A-A' to B-B'), which can be used as a descriptor for the 3.6-km long coastal belt segment, was built up from summations of the three transects into one conflated CBLC sequence: (LME41)7BabiBeca,siDuLitUfoWmr,sw,sl(ER168). The Concised Alongshore BCCS Domain (for the entire coastal belt A-A' to B-B') is reduced by omitting the Large Marine Ecosystem (LME) and Ecoregion (ER) designations to formulate: 7BabiBe-ca,siDuLitUfoWmr,sw,sl. Lastly, the Concised Alongshore Super Domain (for the entire coastal belt A-A' to B-B') is further simplified to the basic archetypical sequence of Barrier-Beach-Dune-Lagoon-Upland-Wetland (Ba-Be-Du-L-U-W), which does not include subscript designators for sub archetypes and translates to a barrier beach-dune system backed by a lagoon (tidal creek) that is bounded landward by a stabilized upland dune system grading into interior wetlands.

Republic of The Gambia (Western Africa, Atlantic Coast)

The results of image interpretation from the tropical Republic of The Gambia coastal belt are shown in the diptych for Figure 5, where Figure 5a is the unannotated image and Figure 5b is the annotated image. The cross-shore cognitive interpretations are shown in the six transects and summarized in Table 7. The swath widths of the transects were determined from both alongshore and cross-shore covisual inspection and interpretation of the eco-geomorphological features. The six domains vary in length from north to south as follows: 476, 917, 2223, 812, 1442, and 378 m, respectively. This dual-phase cognition resulted in the placement of the transect boundaries that were based on marine and terrestrial ecological zones and systems. For example, the third alongshore domain, counted from the northern A-A' boundary southwards, identifies an individual alongshore domain sequence of Ba-Be-Du-W-L-U for that coastal segment. The codifications comprising each transect in Table 7 lists both the Coastal Belt Linked Classification System (CBLC; individual cross-shore transects from A-A' to B-B') and the Biophysical Cross-shore Classification System (BCCS; individual cross-shore transects from A-A' to B-B') sequences.

The Conflated Alongshore CBLC Domain (for the entire coastal belt between A-A' to B-B'), which can be used as a descriptor for the 6-km long coastal belt, was built up from summations of the six transects into one conflated CBLC sequence: (LME27)2,7BabiBecaDuLop,clWma,svUsr(ER113). Whereas the Concised Alongshore BCCS Domain (for the entire coastal belt A-A' to B-B') is reduced by omitting the Large Marine Ecosystem (LME) and Ecoregion (ER) designations to formulate: 2,7BabiBecaDuLWma,svUsr. Lastly, the Concised Alongshore Super Domain (for the entire coastal belt from AA' to B-B') is further simplified to the basic archetypical sequence of Barrier-Beach-Dune-Lagoon-Upland-Wetland (Ba-Be-Du-L-W-U), which does not include subscript designators for sub archetypes and translates to a barrier beach-dune coast backed by lagoons, wetlands, and uplands.

ANALYSIS

The Biophysical Cross-shore Classification System (BCCS) was devised as a means for assessing shore-normal eco-geomorphological successions from offshore to onshore transects within a coastal belt (Finkl and Makowski, 2020a). The three-dimensional (3D) transects were parameterized in terms of alongshore length, cross-shore width, and depth below or elevation above sea level to codify biophysical environments and habitats in the framework of the BCCS. Repetitive ecological successions were so prominent that they were identified as archetypes, which included Barrier, Beach, Beach Ridge, Cliff, Coral Reef, Delta, Dune, Flat, Ice, Lagoon, Mountain, Rock, Till (glacial material), Upland, and Wetland. By sequentially linking together several archetypes based on a cross-shore ecological interpretation of the satellite imagery, a common master sequence, or catena, was generated and referred to as a Dominant Catenary Sequence (DCS; e.g., Beach-Dune-Wetland). The more detailed coastal ecological sequence (CES) of a coastal belt, which is defined by a discrete codification sequence built up from the DCS, is formulated by cognitive geovisual-analytics to link the DCS with a numbered shore-parallel shape configuration and subscripted sub archetypes to refine the sequential composite archetypes in the master dominant catena (Table 1).

The Coastal Belt Linked Classification (CBLC), on the other hand, is based on the BCCS, CES, and DCS by building the cross-shore concatenations with the addition of marine and terrestrial ecological designations in the form of Large Marine Ecosystems (LME) and terrestrial Ecoregions (ER) (Finkl and Makowski, 2020d). This linkage is formed to complete a comprehensive eco-geomorphological description from offshore to onshore inland features. The cross-shore sequencing of archetypes and sub archetypes is based on shore-normal transects that are of variable widths, as shown in Figures 15. The width of each transect is determined by the naturally-occurring properties of the archetypes and sub archetypes as determined from interpretation of satellite images obtained from the Google Earth Pro platform. Cognitive inspection of the satellite imagery provides the basis for estimating the lateral (shore-parallel) extent of the transect, which can also be referred to as its width. The width of the transect thus spans a specified alongshore distance that is referred to as a domain for classificatory purposes. Domain boundaries are determined initially alongshore by changes in the presence of archetypes and sub archetypes, as for example seen in Figure 1b, where a barrier island (Babi) sub archetype transitions to a delta (De) archetype. Occasionally, the same archetypes extend alongshore without change to other archetypes, as in the continuation of the barrier island (Babi) sub archetypes along the Russian Federation Coastal Belt (Figure 1b), and transect boundaries are then subsequently determined by the sequential occurrence of eco-geomorphological features inland. In this way, adjacent transects do not report the same cross-shore catenas.

The final culmination of describing the entire coastal belt can come as either the Conflated Alongshore CBLC Domain (includes LME, ER, shore-parallel configuration numbers, and sub archetype subscripts), the Concised Alongshore BCCS Domain (includes shore-parallel configuration numbers and sub archetype subscripts), or the Concised Alongshore Super Domain (includes only the Dominant Catenary Sequence [DCS] of archetypes). Each of the satellite images, as shown in Figures 15, is briefly analyzed in the following paragraphs.

Russian Federation Coastal Belt (Laptev Sea, Gulf of Anabar Coast)

Analysis of this polar example of cross-shore archetypical sequences shows that when initial (off- and onshore) archetypes are the same, the transect is further differentiated by sequences extending farther inland. As a result, transects of variable widths are cognitively interpreted from the satellite imagery. The transects may be laterally correlatable immediately alongshore but will produce different cross-shore sequences with distance inland, as shown in Figure 1b, for example. The cross-shore transect in the delta domain differs from all of the domains, where the initial dominant archetype is a delta (De) rather than the sub archetype of a barrier island (Babi). The single delta archetype identified in Figure 1b is succeeded landward by a tidal sand flat (Fsa), open lagoon (Lop), wetland swamp (Wsw), and upland tundra (Utu) sub archetypes. The dominant barrier island sub archetypes found in the other domains alongshore are succeeded inland by a combination of open lagoons (Lop), tidal sand flats (Fsa), wetland swamps (Wsw), and upland tundra (Utu) ecosystems that, when combined with the Large Marine Ecosystem (LME57; Laptev Sea), terrestrial Ecoregion (ER781; Taimyr-Central Siberian Tundra), and shore-parallel configuration numbers (e.g., 2 = curved; 7 = straight; Table 1), expand to formulate the Conflated Alongshore CBLC Domain of (LME57)2,7BabiBesiDeFsaLopWswUtu(ER781) for the entire coastal belt. This classification of the Russian Federation coastal belt can be reduced by omitting the LME and ER linkages to then form the Concised Alongshore BCCS Domain: 2,7BabiBesiDeFsaLopWs-wUtu. The final Concised Alongshore Super Domain, which is devoid of shore-parallel configuration numbers or sub archetypes, represents the overall alongshore dominant catenary sequence for the entire coastal belt with: Ba-Be-De-F-L-W-U (cf., Table 3).

Estonia Coastal Belt (Baltic Sea, Gulf of Finland Coast)

The subpolar coastal belt of Estonia shown in Figure 2 is an example of a barrier island (Babi) – beach ridge strandplain (Brsp) eco-geomorphological succession. Analysis of the catenas is based on interpretation of the cross-shore transects and their conversion to alongshore domains. All of the cross-shore catenas and alongshore domains shown in Figure 2b are also listed in Table 4. The dominant barrier island (Babi) sub archetypes found in all the domains alongshore are succeeded inland by a variable combination of silica beaches (Besi), dunes (Du), wetland marshes (Wmr), upland scrub vegetation (Usr), and beach ridge strandplain (Brsp) ecosystems that, when combined with the Large Marine Ecosystem (LME23; Baltic Sea), terrestrial Ecoregion (ER679; Sarmatic Mixed Forests), and shore-parallel configuration number (e.g., 7 = straight; Table 1), expand to formulate the Conflated Alongshore CBLC Domain of (LME23)7BabiBesiDuBrspUsrWmrBrsp(ER679) for the entire coastal belt. This classification of the Estonia coastal belt can be reduced by omitting the LME and ER linkages to then form the Concised Alongshore BCCS Domain: 7BabiBesi-DuBrspUsrWmrBrsp. The final Concised Alongshore Super Domain, which is devoid of shore-parallel configuration numbers or sub archetypes, represents the overall alongshore dominant catenary sequence for the entire coastal belt with: Ba-Be-Du-Br-U-W-Br (cf., Table 4).

United States Coastal Belt (State of Oregon, Pacific Coast)

The middle latitude example shown in Figure 3 is a United States coastal belt along the Pacific coast featuring a headland bay beach. Analysis of the catenas is based on interpretation of the cross-shore transects and their conversion to alongshore domains. All of the cross-shore catenas and alongshore domains shown in Figure 3b are also listed in Table 5. The dominant sub archetypes found in the alongshore domains are variable and include: igneous cliffs (Clig), sedimentary cliffs (Clse), beach ramparts (Berp), rock talus and scree (Rts), rock platforms (Rpl), and silica beaches (Besi). All of these dominant sub archetypes are succeeded inland by a nonspecific combination of upland forests (Ufo), igneous cliffs (Clig), sedimentary cliffs (Clse), and silica beach (Besi) ecosystems that, when combined with the Large Marine Ecosystem (LME3; California Current), terrestrial Ecoregion (ER351; Central Pacific Northwest Coastal Forests), and shore-parallel configuration numbers (e.g., 2 = curved; 6 = promontories and headlands; 7 = straight; Table 1), expand to formulate the Conflated Alongshore CBLC Domain of (LME3)2,6,7Berp,siRpl,tsClig,seUfo(ER351) for the entire coastal belt. The classification of this United States (Pacific) coastal belt can be reduced by omitting the LME and ER linkages to then form the Concised Alongshore BCCS Domain: 2,6,7Berp,siRpl,tsClig,seUfo. The final Concised Alongshore Super Domain, which is devoid of shore-parallel configuration numbers or sub archetypes, represents the overall alongshore dominant catenary sequence for the entire coastal belt with: Be-R-Cl-U (cf., Table 5).

Australian Coastal Belt (Queensland, Coral Sea Coast)

The subtropical Australian coastal belt found along the Coral Sea Coast in Queensland is an example of a long, straight, sandy barrier beach backed by dunes, waterways, and wetlands. Analysis of the catenas is based on interpretation of the cross-shore transects and their conversion to alongshore domains. All of the cross-shore catenas and alongshore domains shown in Figure 4b are also listed in Table 6. The dominant barrier island (Babi) sub archetypes found in all the domains alongshore are succeeded inland by a variable combination of carbonate beaches (Beca), silica beaches (Besi), dunes (Du), intermittently-closed lagoons (Lit), upland forests (Ufo), and wetland marsh (Wmr), swamp (Wsw), and salina/salt flat (Wsl) ecosystems that, when combined with the Large Marine Ecosystem (LME41; East-Central Australian Shelf), terrestrial Ecoregion (ER168; Eastern Australian Temperate Forests), and shore-parallel configuration number (e.g., 7 = straight; Table 1), expand to formulate the Conflated Alongshore CBLC Domain of (LME41)7BabiBeca,siDuLitUfoWmr,sw,sl(ER168) for the entire coastal belt. This classification of the Australian coastal belt can be reduced by omitting the LME and ER linkages to then form the Concised Alongshore BCCS Domain: 7BabiBeca,siDuLitUfoWmr,sw,sl. The final Concised Alongshore Super Domain, which is devoid of shore-parallel configuration numbers or sub archetypes, represents the overall alongshore dominant catenary sequence for the entire coastal belt with: Ba-Be-Du-L-U-W (cf., Table 6).

Republic of The Gambia Coastal Belt (Western Africa, Atlantic Coast)

This tropical example of a coastal belt in the Republic of The Gambia, West Africa, is shown in Figure 5. Analysis of the catenas is based on interpretation of the cross-shore transects and their conversion to alongshore domains. All of the cross-shore catenas and alongshore domains shown in Figure 5b are also listed in Table 7. The two dominant sub archetypes found in the alongshore domains include barrier islands (Babi) and carbonate beaches (Beca). These two dominant sub archetypes are then succeeded inland by a variable combination of carbonate beaches (Beca), dunes (Du), open lagoons (Lop), closed lagoons (Lcl), upland scrub vegetation (Usr), and wetland mangrove forest (Wma) and submerged vegetation (Wsv) ecosystems that, when combined with the Large Marine Ecosystem (LME27; Canary Current), terrestrial Ecoregion (ER113; Guinean Mangroves), and shore-parallel configuration numbers (e.g., 2 = curved; 7 = straight; Table 1), expand to formulate the Conflated Alongshore CBLC Domain of (LME27)2,7BabiBecaDuLop,clWma,svUsr(ER113) for the entire coastal belt. This classification of a tropical coastal belt can be reduced by omitting the LME and ER linkages to then form the Concised Alongshore BCCS Domain: 2,7BabiBecaDuLop,cl Wma,svUsr. The final Concised Alongshore Super Domain, which is devoid of shore-parallel configuration numbers or sub archetypes, represents the overall alongshore dominant catenary sequence for the entire coastal belt with: Ba-Be-Du-L-W-U (cf., Table 7).

DISCUSSION

Coastal classification is notoriously fraught with multiple difficulties. This is because the coast is an inherently complicated area that merges marine, onshore (coastal interfaces), and terrestrial ecosystems into a belt of eco-geomorphological successions both cross-shore and alongshore. Because of these complexities, many methods of coastal classification and zonation have been developed over time to meet the special needs of researchers, scientists, and managers (e.g., Ahrendt et al., 2008; Bailey, 1998; Bartley, Buddemeier, and Bennett, 2001; Burke et al., 2001; Cooper and McLaughlin, 1998; Dinerstein et al., 2017; Dolan et al., 1972; Fairbridge, 2004; Finkl, 2004; Finkl and Makowski, 2020a,b,c,d; Hayden, Ray, and Dolan, 1984; Kelletat, 1989, 1995; Kelletat, Scheffers, and May, 2013; Makowski, 2014; Makowski, Finkl, and Vollmer, 2015, 2016, 2017; McGill, 1958; Pilkey, 2003; Scheffers, Scheffers, and Kelletat, 2012; Sherman, Aquarone, and Adams, 2009; Short, 2000; Short and Woodroffe, 2009). The intricate nature of coastal belts demands specialized investigations and a multiplicity of classifications, and no single or universal approach to the characterization and designation of coasts is completely intrinsic. That is to say, there is no superior system of coastal classification that meets the needs of all users.

For many coastal researchers, there has long been a desire to approach the classification of coasts in such a way that some notion of cross-shore variability can be comprehended in preference to a single abiotic descriptor, such as a sandy, rocky, muddy, dune, or cliffy coast; however, that does not convey relevant successional information related to biophysical features lying immediately inland. Some terms such as barrier island entrain general perceptions of integrated ecosystems (e.g., beaches, dunes, lagoons, and wetlands) because these features have been intensively studied the world over are commonly encountered from tropical to polar latitudes (e.g., Cooper, Lewis, and Pilkey, 2007; Cooper and McLaughlin, 1998; Otvos, 2005; Pilkey, 2003; Short and Woodroffe, 2009; Stutz and Pilkey, 2001). In spite of the apparent simplicity of recognizing barrier island set ups, the identification is complicated by the transient association of barrier islands and spits per se and sensu lato, which implies a degree of subjectivity in temporal designations. Although other examples can also be cited regarding spatiotemporal changes in the coastal zone, the point taken is that all coastal classifications are perception approximations of (often rapidly) changing conditions along the land-sea boundary.

Cross-Shore vs. Alongshore Classification

Accepting the variability of coastal conditions over time and space, this study's line of research was initially investigated in an effort to link together continuums across-the-shore in 3D space to supplement alongshore classifications and descriptions. The results of these efforts were reported by Finkl and Makowski (2020a,b,c,d), who developed approaches to cross-shore classification of commonly repeating eco-geomorphological features by referring to them as archetypes. The distinctive sequences of archetypes across-the-shore to variable distances inland was then recognized as catenary successions, or catenas. These catenas were found to be consistent across multiple latitudes and limited in the number of variabilities so that they could be recognized the world over. Realizing that cross-shore variation was finite and not infinite made it possible to classify coastal belts through archetypical catena codification, which became known as the Biophysical Cross-shore Classification System (BCCS; Finkl and Makowski, 2020a). Although useful, the BCCS was subsequently expanded to include adjacent marine and terrestrial ecosystems by linking the designations for Large Marine Ecosystems (LME) and terrestrial Ecoregions (ER) in the Coastal Belt Linked Classification (CBLC; Finkl and Makowski, 2020d).

It eventually became apparent that the shore-normal transects had fixed widths, thereby allowing for a delineation of domains in order to derive an alongshore classification based on the cross-shore transectal catenas. The procedure for conducting alongshore classifications based on across-shore transects involves recognizing the presence of archetypes and sub archetypes immediately onshore and then observing archetypical sequencing in the satellite image with distances inland. Shore-normal boundary lines can then be used to define the width of the resulting sequence and the coastal belt can be subdivided in terms of cross-shore transects of variable widths, thus establishing the domains of the coastal belt.

These procedural applications were predicated on concepts related to the correlation of geological borehole logs and applied to cross-shore transectal codifications in an analogous way. Whereas correlations with geologic drilling and petrophysical logging are based on depth functions, the procedure in this study was to laterally correlate adjacent surficial cross-shore transects. The biophysical features making up the cross-shore catenas (Table 1) were correlatable because they were finite in number and repeatable. Analogously following the geological borehole (well log) stratigraphic correlation, shore-parallel correlations between cross-shore transects thus resulted in an alongshore codification and classification.

Super Domain Delineation of Coastal Belts

Determination of archetypes via cognitive interpretation of satellite images is based on the ability to readily discern biophysical features within a coastal belt, such as barrier, beach, beach ridge, cliff, coral reef, delta, dune, flats, ice, lagoon, mountain, rock, till, upland, and wetland archetypical variants (see Table 1). This list of archetypes is by no means fixed and can be added to or edited based on the needs of the classification and the biophysical composition of the coastal belt being classified. Regarding the use (and definition) of sub archetypes, some collateral knowledge is beneficial when indicating lithologies, beach sand composition, whether lagoons are intermittently open or closed, and defining various types of uplands, wetlands, and dunes. Reference to Large Marine Ecosystems (LMEs) and terrestrial Ecoregions (ERs), as well as other ancillary data, can be easily assimilated by reading source materials or accessing online maps and databases (e.g., Asch, 2003; Dinerstein et al., 2017; Peters, Husson, and Czaplewski, 2018; Thiéblemont, 2016).

The placement of the cross-shore transect and alongshore domain boundaries is more subjective and depends on the scale of the imagery, and the degree of specificity required. The cross-shore transect lines that extend from offshore to variable distances inland are not static since they are general indications of the inland lateral extents of the transects. They are thus used as a guide to determine the general occurrence of archetypes and sub archetypes that are used to make up the cross-shore catena sequence. The boundaries of cross-shore transectal sections become more diffuse with distance inland as the farther they go the less relevant they become to characterization of alongshore domains. The distances that cross-shore transects extend inland should be based on the relevance of the eco-geomorphological features being classified as they relate to the alongshore classification of the coastal belt. In the examples provided in Figures 15, the cross-shore transects extended only a few kilometers inland as this was sufficient to characterize the coastal belts and provide alongshore domain classification sequences. Even though exceptions occur, extension of the cross-shore transects farther inland is usually not useful, as it provides added complexity to an already complicated environment.

Other caveats to the alongshore classification of coastal belts based on the interpretation of satellite imagery relate to the acquired imagery. Some images obtained from the Google Earth Pro platform may require a degree of enhancement for better optical presentation and then there is the conversion of file types from one computer program to another. In the examples presented in this study, most of the images were handled in various ways using programs such as Photoshop, GIMP, and PowerPoint once they were accessed from Google Earth Pro. Because there are so many possibilities for enhancing and studying the satellite imagery, researchers are encouraged to use their preferred programs.

The idea that 3D coastal swaths can be used for coastal classification versus 1D passes is not a new concept, as it was introduced and applied some decades ago by McGill (1958), and more recently by Ahrendt et al. (2008). However, the current free availability of quality satellite imagery from Google Earth Pro introduces a whole new opportunity for coastal classification. Although there may be other possibilities for obtaining satellite imagery, the advantage of the Google Earth Pro platform is its worldwide coverage of remote coastal belt locations. One disadvantage is that sometimes the images are stitched together from multiple satellite passes leaving uncorrected color contrast lines that may require some complicated image enhancements or ascetically unpleasing scenes. Even so, this is normally not the case and may depend on the selection of the coastal belt location.

Once the satellite imagery was acquired and interpreted, the information presented in this study was collated and displayed in a simplistic manner to best show the application of cross-shore classification via transects to the development of classificatory alongshore domains. The Conflated Alongshore CBLC Domain, which can be used as a descriptor for the entire coastal belt segment, was built up from summations of all the cross-shore transects into one conflated alongshore sequence that is based on the coastal belt linked classification system (CBLC; Finkl and Makowski, 2020d). The Concised Alongshore BCCS Domain (for the entire coastal belt) is based on the Biophysical Cross-shore Classification System (BCCS; Finkl and Makowski, 2020a) and simplifies the Conflated Alongshore CBLC Domain by removing the representation of the Large Marine Ecosystem (LME) and terrestrial Ecoregion (ER). Ultimately, the Concised Alongshore Super Domain (for the entire coastal belt) is further simplified to the basic archetypical sequence (Dominant Catenary Sequence, or DCS), which does not include subscript designators for sub archetypes. The succinct simplicity of the Concised Alongshore Super Domain provides a valuable alongshore classification (based on cross-shore transect interpretation) that can be effectively applied to coastal belts of all latitudinal ranges worldwide.

CONCLUSION

Development of an experimental cross-shore classification scheme, based on the interpretation of satellite images, led to the conceptualization of alongshore domains. One example from multiple latitudinal zones (i.e. polar, subpolar, middle latitude, subtropical, tropical) showed that the cross-shore codification of eco-geomorphological features can be formalized using the Biophysical Cross-shore Classification System (BCCS; Finkl and Makowski, 2020a). This is achieved by utilizing commonly reoccurring biophysical (ecological) archetypes, such as barriers, beaches, beach ridges, cliffs, coral reefs, deltas, dunes, flats, ice, lagoons, mountains, rock, till, uplands, and wetlands (Table 1). Subdivision of archetypes resulted in the recognition of sub archetypes that increased the codified specificity of cross-shore transectal sequences (catenas) from offshore marine to inland terrestrial environments. When these BCCS units were combined with Large Marine Ecosystem (LME) and terrestrial Ecoregion (ER) designations, a complete ecological classification for each cross-shore transect resulted by following the Coastal Belt Linked Classification (CBLC; Finkl and Makowski, 2020d). The Conflated Alongshore CBLC Domain is then built up from the summation of all the cross-shore transects into one conflated alongshore sequence for the entire coastal belt. The Concised Alongshore BCCS Domain further simplifies the Conflated Alongshore CBLC Domain by removing the representation of the Large Marine Ecosystem (LME) and terrestrial Ecoregion (ER).

In order to delineate the individual domain widths for the purpose of a super domain alongshore classification, each cross-shore transect was reduced to a Dominant Catenary Sequence (DCS) of archetypes (Tables 37). The multiplicity of contiguous domains, each representing a cross-shore transect swath, was used to define alongshore classification of the entire coastal belt. The Concised Alongshore Super Domain (for the entire coastal belt) is further simplified to the basic archetypical sequence by not including subscript designators for sub archetypes. The results of this study concluded that the sequencing of coastal archetypes and sub archetypes can be beneficial to the increased understanding of coastal belt classification worldwide as a function of both cross-shore transect and alongshore domain interpretation from satellite imagery.

LITERATURE CITED

LITERATURE CITED
Ahrendt,
K.;
Scalise,
A.;
Sterr,
H.;
Müller,
F.,
and
Ruljevic,
I.,
2008
.
A new multifunctional coastal classification for eco-system-service assessments.
Natural Resources Conservation and Research.
doi:10.24294/nrcr.v0i0.984
Asch,
K.,
2003
.
The 1:5 million international geological map of Europe and adjacent areas: Development and implementation of a GIS-enabled concept.
In
:
Geologisches Jahrbuch
, SA 3, 147/3161904.
Stuttgart
:
E. Schweizerbart'sche Verlagsbuchhandlung
,
172
p.
Bailey,
R.G.,
1998
.
Ecoregions: The Ecosystem Geography of the Oceans and Continents.
New York
:
Springer
,
176
p.
Bartley,
J.D.;
Buddemeier,
R.W.,
and
Bennett,
D.A.,
2001
.
Coastline complexity: A parameter for functional classification of coastal environments.
Journal of Sea Research
,
46
(
2
),
87
97
.
Burke,
L.A.;
Kura,
Y.;
Revenga,
C.;
Spalding,
M.,
and
McAllister,
D.,
2001
.
Coastal Ecosystems: Pilot Analysis of Global Ecosystems.
Washington, D.C.
:
World Resources Institute
,
77
p.
Burrum Coast National Park Management Staff,
2013
.
Burrum Coast National Park Management Statement 2013.
Bundaberg, Queensland
:
Department of National Parks, Recreation, Sport and Racing
,
11
p.
Cooper,
J.A.G.;
Lewis,
D.A.,
and
Pilkey,
O.H.,
2007
.
Fetch-limited barrier islands: Overlooked coastal landforms.
GSA Today
,
17
(
3
),
4
9
.
Cooper,
J.A.G.
and
McLaughlin,
S.,
1998
.
Contemporary multidisciplinary approaches to coastal classification and environmental risk analysis.
Journal of Coastal Research
,
14
(
2
),
512
524
.
Dinerstein,
E.;
Olson,
D.;
Joshi,
A.;
Vynne,
C.;
Burgess,
N.D.;
Wikramanayake,
E.;
Hahn,
N.;
Palminteri,
S.;
Hedao,
P.;
Noss,
R.;
Hansen,
M.;
Locke,
H.;
Ellis,
E.C.;
Jones,
B.;
Barber,
C.V.;
Hayes,
R.;
Kormos,
C.;
Martin,
V.;
Crist,
E.;
Sechrest,
W.;
Price,
L.;
Baillie,
J.E.M.;
Weeden,
D.;
Suckling,
K.;
Davis,
C.;
Sizer,
N.;
Moore,
R.;
Thau,
D.;
Birch,
T.;
Potapov,
P.;
Turubanova,
S.;
Tyukavina,
A.;
de Souza,
N.;
Pintea,
L.;
Brito,
J.C.;
Llewellyn,
O.A.;
Miller,
A.G.;
Patzelt,
A.;
Ghazanfar,
S.A.;
Timberlake,
J.;
Klöser,
H.;
Shennan-Farpón,
Y.;
Kindt,
R.;
Barnekow Lilles,
J.-P.;
van Breugel,
P.;
Graudal,
L.;
Voge,
M.;
Al-Shammari,
K.F.,
and
Saleem,
M.,
2017
.
An ecoregion-based approach to protecting half the terrestrial realm.
BioScience
,
67
(
6
),
534
545
.
Dolan,
R.;
Hayden,
B.P.;
Hornberger,
G.;
Zieman,
J.,
and
Vincent,
M.,
1972
.
Classification of the Coastal Environments of the World, Part I: The Americas.
Washington, D.C.
:
Office of Naval Research, Geography Programs, Technical Report No. 1
,
163
p.
Fairbridge,
R.W.,
2004
.
Classification of coasts.
Journal of Coastal Research
,
20
(
1
),
155
165
.
Finkl,
C.W.,
2004
.
Coastal classification: Systematic approaches to consider in the development of a comprehensive scheme.
Journal of Coastal Research
,
20
(
1
),
166
213
.
Finkl,
C.W.
and
Makowski,
C.,
2015
.
Autoclassification versus cognitive interpretation of digital bathymetric data in terms of geomorphological features for seafloor characterization.
Journal of Coastal Research
,
31
(
1
),
1
16
.
Finkl,
C.W.
and
Makowski,
C.,
2019a
.
Coastal seafloor geomorphological features, classification.
In
:
Finkl,
C.W.
and
Makowski,
C.
(eds.),
Encyclopedia of Coastal Science.
Cham, Switzerland
:
Springer Nature, Encyclopedia of Earth Sciences Series
, pp.
540
549
.
Finkl,
C.W.
and
Makowski,
C.,
2019b
.
Nearshore geomorphological mapping.
In
:
Finkl,
C.W.
and
Makowski,
C.
(eds.),
Encyclopedia of Coastal Science.
Cham, Switzerland
:
Springer Nature, Encyclopedia of Earth Sciences Series
, pp.
1243
1265
.
Finkl,
C.W.
and
Makowski,
C.,
2020a
.
The Biophysical Cross-shore Classification System (BCCS): Defining coastal ecological sequences with catena codification to classify cross-shore successions based on interpretation of satellite imagery.
Journal of Coastal Research
,
36
(
1
),
1
29
.
Finkl,
C.W.
and
Makowski,
C.,
2020b
.
Latitudinal and situational zonation of shore-normal catenary sequences observed from satellite images using the Biophysical Cross-shore Classification System (BCCS).
Journal of Coastal Research
,
36
(
2
),
1
14
.
Finkl,
C.W.
and
Makowski,
C.,
2020c
.
Lateral extrapolation of coastal catenary sequences using the biophysical cross-shore classification system (BCCS) to create shore-parallel situational zonation mapping units.
Journal of Coastal Research
,
36
(
3
),
457
471
.
Finkl,
C.W.
and
Makowski,
C.,
2020d
.
Coastal Belt Linked Classification (CBLC): A system for characterizing the interface between land and sea based on large marine ecosystems, coastal ecological sequences, and terrestrial ecoregions.
Journal of Coastal Research
,
36
(
4
),
677
693
.
Finkl,
C.W.;
Makowski,
C.,
and
Vollmer,
H.,
2014
.
Advanced techniques for mapping biophysical environments on carbonate banks using laser airborne depth sounding (LADS) and IKONOS satellite imagery.
In:
Finkl,
C.W.
and
Makowski,
C.
(eds.),
Remote Sensing and Modeling: Advances in Coastal and Marine Resources, Volume 9.
Dordrecht, The Netherlands
:
Springer, Coastal Research Library (CRL)
, pp.
31
63
.
Franklin,
J.E.
and
Dyrness,
C.T.,
1988
.
Natural Vegetation of Oregon and Washington.
Corvallis, Oregon
:
Oregon State University Press
,
452
p.
Griffith,
S.J.;
Bale,
C.,
and
Adam,
P.,
2008
.
Environmental correlates of coastal heathland and allied vegetation.
Australian Journal of Botany
,
2008
(
56
),
512
526
.
Harrison,
J.C.;
St-Onge,
M.R.;
Petrov,
O.V.;
Strelnikov,
S.I.;
Lopatin,
B.G.;
Wilson,
F.H.;
Tella,
S.;
Paul,
D.;
Lynds,
T.;
Shokalsky,
S.P.;
Hults,
C.K.;
Bergman,
S.;
Jepsen,
H.F.,
and
Solli,
A.,
2011
.
Geological Map of the Arctic.
Ottawa, Ontario, Canada
:
Geological Survey of Canada, Map 2159A
.
doi:10.4095/287868.2/636496
Hayden,
B.P.;
Ray,
G.C.,
and
Dolan,
R.,
1984
.
Classification of coastal and marine environments.
Environmental Conservation
,
11
(
3
),
199
207
.
Kelletat,
D.H.,
1989
.
The question of “zonality” in coastal geomorphology—With tentative application along the East Coast of the USA.
Journal of Coastal Research
,
5
(
2
),
329
344
.
Kelletat,
D.H.
(ed.),
1995
.
Atlas of coastal geomorphology and zonality.
Journal of Coastal Research, Special Issue No. 13
,
286
p.
Kelletat,
D.H.;
Scheffers,
A.M.,
and
May,
S.M.,
2013
.
Coastal environments from polar regions to the tropics: A geographer's zonality perspective.
Geological Society
,
London
,
Special Publications, 388,
pp.
33
57
.
Keyes,
W.S.,
1996
.
A Practical Guide to Borehole Geophysics in Environmental Investigations.
Boca Raton, Florida
:
Routledge, CRC Press
,
192
p.
Kose,
M.
(ed.),
2012
.
Coastal Lagoons of Estonia and in the Central Baltic Sea Region: Development History, Geology and Hydrology, Biodiversity and Nature Conservation Value.
Tartu, Estonia
:
University of Tartu Parnu College, Estonian University of Life Sciences Institute of Agriculture and Environmental Sciences Centre for Limnology
,
142
p.
Makowski,
C.,
2014
.
Development and Application of a New Comprehensive Image-Based Classification Scheme for Coastal and Benthic Environments along the Southeast Florida Continental Shelf.
Boca Raton, Florida
:
Florida Atlantic University, Ph.D. dissertation
,
303
p.
Makowski,
C.
and
Finkl,
C.W.,
2016
.
History of modern seafloor mapping.
In
:
Finkl,
C.W.
and
Makowski,
C.
(eds.),
Seafloor Mapping along Continental Shelves: Research and Techniques for Visualizing Benthic Environments, Volume 13.
Dordrecht, The Netherlands
:
Springer International Publishing, Coastal Research Library (CRL)
, pp.
1
47
.
Makowski,
C.;
Finkl,
C.W.,
and
Vollmer,
H.M.,
2015
.
Geospatially integrated seafloor classification scheme (G-ISCS): A new method for cognitively interpreting benthic biogeomorphological features.
Journal of Coastal Research
,
31
(
2
),
488
504
.
Makowski,
C.;
Finkl,
C.W.,
and
Vollmer,
H.M.,
2016
.
Classification of continental shelves in terms of geospatially integrated physio-graphic realms and morphodynamic zones.
Journal of Coastal Research
,
32
(
1
),
1
34
.
Makowski,
C.;
Finkl,
C.W.,
and
Vollmer,
H.M.,
2017
.
Geoform and landform classification of continental shelves using geospatially integrated IKONOS satellite imagery.
Journal of Coastal Research
,
33
(
1
),
1
22
.
Makowski,
C.
and
Keyes,
P.,
2011
.
Using the benthic ecological assessment for marginal reefs (BEAMR) method to quantify nearshore reef conditions in the southeast Gulf of Mexico.
Journal of Coastal Research
,
27
(
3
),
428
440
.
Makowski,
C.;
Prekel,
S.E.;
Lybolt,
M.J.,
and
Baron,
R.M.,
2009
.
The benthic ecological assessment for marginal reefs (BEAMR) method.
Journal of Coastal Research
,
25
(
2
),
515
522
.
McGill,
J.T.,
1958
.
Map of coastal landforms.
Geographical Review
,
48
,
402
405
.
Meyer,
R.F.
and
Freeman,
P.A.,
2006
.
Siberian Platform: Geology and Natural Bitumen.
U.S. Geological Survey Open-File Report 2006-1316
,
24
p.
Miller,
R.J.;
Raines,
G.L.,
and
Connors,
K.A.,
2002
.
Spatial digital database for the geologic map of Oregon: Geology compiled by G.W. Walker and N.S. MacLeod.
USGS Open-File Report 03-67, scale 1:500,000.
Otvos,
E.G.,
2005
.
Barrier island formation and development modes.
In:
Finkl
C.W.
and
Makowski,
C.
(eds.),
Encyclopedia of Coastal Science.
Cham, Switzerland
:
Springer International Publishing
, pp.
182
187
.
Peel,
M.C.;
Finlayson,
B.L.,
and
McMahon,
T.A.,
2007
.
Updated world map of the Köppen-Geiger climate classification.
Hydrology and Earth System Science
,
11
,
1633
1644
.
doi:10.5194/hess-11-1633-2007
Peters,
S.E.;
Husson,
J.M.,
and
Czaplewski,
J.,
2018
.
Macrostrat: A Platform for Geological Data Integration and Deep-Time Earth Crust Research.
doi:10.31223/osf.io/ynaxw
Pilkey,
O.H.,
2003
.
A Celebration of the World's Barrier Islands.
New York
:
Columbia University Press
,
400
p.
Puuar,
V.
and
Raukas,
A.,
1997
.
Estonia.
In:
Moores,
E.M.
and
Fairbridge,
R.W.
(eds.),
Encyclopedia of European and Asian Regional Geology.
Dordrecht, The Netherlands
:
Springer International Publishing
, pp.
192
202
.
Raymond,
O.L.;
Liu,
S.;
Gallagher,
R.;
Zhang,
W.,
and
Highet,
L.M.,
2012
.
Surface Geology of Australia.
Canberra, Australia
:
Commonwealth of Australia (Geoscience Australia), 1:1 million scale dataset 2012 edition 5/693929
.
Scheffers,
A.M.;
Scheffers,
S.R.,
and
Kelletat,
D.H.,
2012
.
The Coastlines of the World with Google Earth: Understanding our Environment, Volume 2.
Dordrecht, The Netherlands
:
Springer International Publishing, Coastal Research Library (CRL)
,
293
p.
Schlüter,
T.,
2008
.
The Gambia.
In:
Geological Atlas of Africa: With Notes on Stratigraphy, Tectonics, Economic Geology, Geohazards, Geosites and Geoscientific Education of Each Country.
Heidelberg, Germany
:
Springer
, pp.
114
115
.
Sherman,
K.;
Aquarone,
M.C.,
and
Adams,
S.
(eds.),
2009
.
Sustaining the World's Large Marine Ecosystems.
Gland, Switzerland
:
International Union for Conservation of Nature and Natural Resources (IUCN)
,
142
p.
Short,
A.D.,
2000
.
Beaches of the Queensland Coast: Cooktown to Coolangata: A Guide to their Nature, Characteristics, Surf and Safety.
Sydney
:
University of Sydney
,
279
p.
Short,
A.D.
and
Woodroffe,
C.D.,
2009
.
The Coast of Australia.
Cambridge
:
Cambridge University Press
,
288
p.
Stutz,
M.L.
and
Pilkey,
O.H.,
2001
.
A review of global barrier island distribution.
Journal of Coastal Research, Special Issue No. 34
, pp.
15
22
.
Thiéblemont,
D.
(ed.),
2016
.
New Edition of the 1:10,000,000 Geological Map of Africa. CGMW-BRGM, 190/3311225, Macrostrat online version.
Timm,
T.;
Kumari,
M.;
Kübar,
K.;
Sohar,
K.,
and
Traunspurger,
W.,
2007
.
Meiobenthos of some Estonian coastal lakes.
Proceedings of the Estonian Academy of Sciences, Biology and Ecology
,
56
(
3
),
179
195
.
Vilumaa,
K.;
Tõnisson,
H.;
Kont,
A.,
and
Ratas,
U.,
2013
.
Ground-penetrating radar studies along the coast of Estonia.
In
:
Conley,
D.C.;
Masselink,
G.;
Russell,
P.E.,
and
O'Hare,
T.J.
(eds.),
Proceedings from the International Coastal Symposium (ICS) 2013. Journal of Coastal Research, Special Issue No. 65
, pp.
612
617
.
Weaver,
C.E.;
Beck,
S.;
Bramlette,
M.N.;
Carlson,
S.;
Clark,
B.L.;
Dibblee,
T.W.;
Jr.,
Durham,
W.;
Ferguson,
G.C.;
Forest,
L.C.;
Grant,
U.S.;
IV,
Hill,
M.;
Kelley,
F.R.;
Kleinpell,
R.M.;
Kleinpell,
W.D.;
Marks,
J.;
Putnam,
W.C.;
Schenck,
H.G.;
Taliaferro,
N.L.;
Thorup,
R.R.;
Watson,
E.;
White,
R.T.,
and
The Western Cenozoic Subcommittee,
1944
.
Correlation of the marine Cenozoic formations of western North America [Chart 11].
Geological Society of America Bulletin
,
55
(
5
),
569
598
.
Wonik,
T.
and
Olea,
R.A.,
2007
.
Borehole logging.
In:
Knödel,
K.;
Lange,
G.,
and
Voigt,
H-J.
(eds.),
Environmental Geology: Handbook of Field Methods and Case Studies.
Berlin
:
Springer
, pp.
475
505
.