Integrating a Three-Level GIS Framework and a Graph Model to Track, Represent, and Analyze the Dynamic Activities of Tidal Flats

International Journal of Geo-Information

Article Integrating a Three-Level GIS Framework and a Graph Model to Track, Represent, and Analyze the Dynamic Activities of Tidal Flats

Chao Xu and Weibo Liu *

Department of Geosciences, Florida Atlantic University, Boca Raton, FL 33431, USA; [email protected] * Correspondence: [email protected]; Tel.: +1-561-297-4965

Abstract: Tidal flats (non-vegetated area) are soft-sediment habitats that are alternately submerged and exposed to the air by changeable tidal levels. The tidal flat dynamics research mainly utilizes the cell-level comparisons between the consecutive snapshots, but the in-depth study requires more detailed information of the dynamic activities. To better track, represent, and analyze tidal flats’ dynamic activities, this study proposes an integrated approach of a three-level Geographic Information Science (GIS) framework and a graph model. In the three-level GIS framework, the adjacent cells are assembled as the objects, and the objects on different time steps are linked as lifecycles by tracking the predecessor–successor relationships. Furthermore, eleven events are defined to describe the dynamic activities throughout the lifecycles. The graph model provides a better way to represent the lifecycles, and graph operators are utilized to facilitate the event

analysis. The integrated approach is applied to tidal ﬂats’ dynamic activities in the southwest tip of Florida Peninsula from 1984 to 2018. The results suggest that the integrated approach provides an

Citation: Xu, C.; Liu, W. Integrating effective way to track, represent, and analyze the dynamic activities of tidal flats, and it offers a novel a Three-Level GIS Framework and a perspective to examine other dynamic geographic phenomena with large spatiotemporal scales. Graph Model to Track, Represent, and Analyze the Dynamic Activities of Keywords: three-level GIS framework; graph model; dynamic activities; lifecycle; tidal flats Tidal Flats. ISPRS Int. J. Geo-Inf. 2021, 10, 61. https://doi.org/10.3390/ ijgi10020061 1. Introduction Academic Editors: Wolfgang Kainz, The study of dynamic geographic phenomena is a challenging topic in Geographic Cristina Ponte Lira and Information Science (GIS). In this topic, the core issue is the spatiotemporal characteristics of Rita González-Villanueva the study subjects, and the prerequisite is to identify and delineate the study subjects from Received: 8 December 2020 the source data. Compared with the delineation, a more critical problem is how to track Accepted: 28 January 2021 better, represent, and analyze the dynamic activities from the delineated results, which can Published: 1 February 2021 help obtain the relevant information to fully describe the spatiotemporal characteristics of dynamic geographic phenomena and explain the reasons behind them. Publisher’s Note: MDPI stays neutral In the GIS community, modeling the dynamic activities of geographic phenomena has with regard to jurisdictional claims in published maps and institutional affil- been discussed, developed, and refined during the past several decades. As summarized iations. by Worboys (2005) [1], this procedure can be divided into three stages: (1) the temporal snapshots, (2) the object changes, and (3) the events and action. As the delineated results can represent the static state of the dynamic geographic phenomena at one single moment, the first stage makes it possible to find the temporal sequences of objects, their attributes, and relationships by associating the states at different moments along the time sequence. Copyright: © 2021 by the authors. For instance, Armstrong (1988) [2] proposes the snapshot view, which allows the query for Licensee MDPI, Basel, Switzerland. the temporal information of a single object and all items associated with a given time. In This article is an open access article addition, Worboys (2005) [1] points out that the time domain should allow interpolation be- distributed under the terms and conditions of the Creative Commons tween measurements in case of the continuous movements. However, the researchers often Attribution (CC BY) license (https:// feel frustrated when considering the changes, because the first stage focuses on the tempo- creativecommons.org/licenses/by/ ral sequences of snapshots only [3]. In response to the new challenges, the second stage 4.0/). shifts the focus to the changes retrieved from the series of temporal snapshots. Hornsby

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and Egenhofer (2000) [4] emphasize that the change identification should be based on the comparison between the subsequent snapshots. As one of the earliest attempts, Peuquet and Duan (1995) [5] propose a data model that explicitly represents the changes based on the temporal comparisons in sequence. On the other hand, Claramunt and Thériault (1995) [6] propose a temporal framework focused on the geographic entities’ topological changes, which provides a novel perspective to examine the complex evolution of dynamic geographic phenomena. However, the second stage’s changes can only contribute to the preliminary representation and analysis for the dynamic geographic phenomena, and therefore, the third stage is developed in response to the new challenges. As a breakthrough, Yuan (2001) [7] systematically organizes a framework in which a temporal sequence of states forms a process, and a temporal aggregation of the processes makes an event. In the third stage, Worboys (2005) [1] highlights “the continuants that endure through time” and “the occurrents that happen or occur and are then gone”. This perception further deepens and expands the concept of objects in the second stage, and it yields the event- based perspective, which provides a possible way to better model the dynamic geographic phenomena. The objects can be tracked as lifecycles through which the dynamic activities can be observed [8]. McIntosh and Yuan (2005) [9] propose a four-level framework (zone, sequence, process, and event), which facilities the spatiotemporal query and analysis for the distributed dynamic geographic phenomena, and a case study of rainstorms is implemented. In addition, the following studies of marine dunes [10], ocean eddies [11], convective storms [12], urban heat islands [13], and Land Use and Land Cover (LULC) [14] have also endorsed the good performance of the event-based model. However, due to the data availability and computing performance, the previous studies often face the challenges of domain selection and spatiotemporal scale limitation. The in-depth study of event-based model is expected to eliminate these shortcomings with the aid of high-performance computing. As a following issue, a good representation strategy could not only contribute to the visualization of the lifecycles but also facilitate the further analysis of the dynamic activities. More recently, the graph theory has been applied to GIS studies, which provides a possible solution to better represent the lifecycles. Thibaud et al. (2013) [10] utilizes a spatiotemporal graph model, in which “the graph edges are associated with the concepts of spatial relations and filiation relations”. Cheung et al. (2015) [15] suggests that a sequence of snapshots at particular time steps can be threaded together into a single spatiotemporal graph, which provides an enhanced way to visualize the evolutionary trajectories over time. Furthermore, Wu et al. (2016) [16] proves that the graph theory can demonstrate a concise and intuitionistic way to capture and represent the storm-induced coastal changes. As the lifecycles are represented as graphs, the dynamic activities have great potential to get further analyzed with graph operators and algorithms. Previous studies mainly concentrate on the representation and visualization of dynamic geographic phenomena via the graph model, so it needs further study on how to adapt the graph algorithms to fully explore the dynamic activities in different domains. In this study, we select the tidal flat as the subject of research. It is the coastal sediment that is alternately and periodically submerged in water and exposed to the air by the changeable tidal levels. As the land–sea interactions take place, the temperature, salinity, and acidity in the tidal flat region become variable [17]. A unique type of wetland ecosystem is the product under the above physical and chemical conditions, which is the homeland of shorebird [18], fungus [19], plankton [20], coastal fish [21], and so on. Aside from its contribution to biodiversity, the tidal flat also plays an important role in carbon preservation and global warming prevention [22–26]. In addition, the tidal flat has economic benefits. The unvegetated tidal flat all over the world contributes a total of $2.44 trillion USD per year as of the value in the year 2011 [27]. Hence, the dynamic activities of tidal flats have attracted the attention of researchers from different backgrounds. It is essential to clarify the scope of the tidal flat region in this study because it can be defined in two ways. In a broad sense, the tidal flat region includes the bare intertidal flats, ISPRSISPRS Int. Int. J. J. Geo-Inf. Geo-Inf. 20212021, ,1010, ,x 61 FOR PEER REVIEW 33 of of 23 23

It is essential to clarify the scope of the tidal flat region in this study because it can be defined in two ways. In a broad sense, the tidal flat region includes the bare intertidal flats,as well as aswell the as vegetated the vegetated flats covered flats covered by salt by marshes, salt marshes, mangroves, mangroves, and seagrasses and seagrasses [28,29]. [28,29].In a narrow In a sense,narrow the sense, tidal the flat regiontidal flat only region includes only the includes unvegetated the unvegetated intertidal flats intertidal [30,31]. flatsThis [30,31]. study explicitly This study aims explicitly at thenarrow aims at sensethe narrow of tidal sense flats of (Figure tidal 1flats). (Figure 1).

FigureFigure 1. 1. AnAn illustration illustration of of the the tidal tidal flat ﬂat region region used used in in this this study. study.

FollowingFollowing the the existed existed theoretical theoretical basis, basis, it it is is expected expected to to propose propose a a novel approach thatthat facilitates facilitates the the acquisition acquisition and and summarization for for the spatiotemporal characteristics ofof tidal tidal flat flat dynamic dynamic activities. activities. More More specifically, specifically, the the goal goal of of this this study study is is to to integrate integrate a a three-levelthree-level GIS GIS framework framework and and graph graph model. model. The The three three levels levels are are as as follows: follows: (1) (1) the the cell cell level,level, which which is the satellitesatellite imageimage pixelpixel of of the the tidal tidal flat; flat; (2) (2) the the object object level, level, which which is theis the set setof contiguousof contiguous tidal tidal flat cellsflat cells with with a specific a spec stateific atstate a specific at a specific moment; moment; and (3) theand lifecycle (3) the lifecyclelevel, which level, is which the set is ofthe tidal set of flat tidal objects flat objects at different at different time steps time linked steps linked as a chain as a basedchain basedon the on relative the relative position position relationships. relationships. In addition, In addition, different different events to events depict to the depict dynamic the dynamicactivities activities can be derived can be from derived the lifecyclefrom the level. lifecycle Therefore, level. Therefore, this three-level this three-level GIS framework GIS frameworkshould be capable should ofbe tidal capable flat lifecycleof tidal tracking,flat lifecycle which tracking, is the foundation which is the of eventsfoundation capture. of eventsOn the capture. other hand, On the the other graph hand, model the can graph convert model the can spatiotemporal convert the spatiotemporal information in thein- formationlifecycle level in the to thelifecycle directed level graphs, to the and direct itsed derivative graphs, spatiotemporaland its derivative database spatiotemporal offers the databasestorage and offers query the featuresstorage forand the query morphologic features for attributes the morphologic of the tidal attributes flats throughout of the tidal each flatsindividual throughout lifecycle. each Consequently, individual lifecycle. the graph Consequently, can provide the an enhancedgraph can way provide to represent an en- hancedthe lifecycles way to and represent analyze the the lifecycles dynamic and activities analyze of the tidal dynamic flats. activities of tidal flats. AA case case study study is is applied applied to the tidal flatsflats in thethe southwestsouthwest tiptip ofof thethe FloridaFlorida peninsulapeninsu- lafrom from 1984 1984 to to 2018. 2018. Owing Owing to to the the development development of of high-performance high-performance cloud cloud computing computing in inrecent recent years, years, it it has has become become feasible feasible to to access, access, process,process, andand analyze geospatial big big data, data, andand therefore, ourour integratedintegrated approach approach can can be appliedbe applied to the to largerthe larger spatiotemporal spatiotemporal scales. scales.With the With available the available big datasets, big datasets, the machine the ma learningchine learning algorithms algorithms are widely are usedwidely in cloudused incomputing cloud computing platforms. platforms. The random The random forest (RF) forest machine (RF) machine learning learning algorithm algorithm is used tois delineate the cell level tidal flat information in this study. used to delineate the cell level tidal flat information in this study. 2. Methods 2. Methods 2.1. Tidal Flat Cell Delineation 2.1. Tidal Flat Cell Delineation To study the spatiotemporal characteristics, it is essential to delineate the cells of the dynamicTo study phenomenathe spatiotemporal from the characteristics, satellite data it [ 11is ].essential As different to delineate types the of LULC cells of have the dynamicdifferent phenomena spectral characteristics, from the satellite the remote data sensing-derived [11]. As different spectral types indicesof LULC are have commonly differ- entused spectral for water characteristics, body identification, the remote which sensing-derived is the prerequisite spectral for tidal indices flat cellare delineation.commonly usedThese for indices water includebody identification, the Normalized which Difference is the prerequisite Water Index for (NDWI) tidal flat [32 cell], Land delineation. Surface TheseWater indices Index (LSWI)include [ 33the], ModifiedNormalized Normalized Difference Difference Water Index Water (NDWI) Index [32], (MNDWI) Land Sur- [34], faceAutomated Water Index Water (LSWI) Extraction [33], Index Modified (AWEI) No [rmalized35], and soDifference on. Water Index (MNDWI) [34], AutomatedBased on water Water body Extraction identification Index (AWEI) results, [35], many and effortsso on. have been spent on de- lineatingBased tidal on water flats frombody theidentification satellite images. results, The many conventional efforts have methods been spent include on deline- linear atingregression tidal flats [36], Gaussianfrom the satellite function images. [37], Otsu The thresholding conventional [38 methods,39], and include frequency linear thresh- re- ISPRS Int. J. Geo-Inf. 2021, 10, 61 4 of 23

olding [29,40,41]. Compared with the above conventional methods, the RF could classify the cells with respect to multiple indices and their statistical distributions and provide higher robustness [42]. To date, the RF is widely used in LULC classification issues, such as cropland [43,44], woody vegetation [45], human settlement [46,47], coastal lands [48], and so on. For tidal flat cell delineation, several methods have been developed based on the RF. Zhang et al. (2019)’s RF-based method [28] used MNDWI, Normalized Difference Vegetation Index (NDVI) [49], LSWI, Enhanced Vegetation Index (EVI) [50], Modified Soil- Adjusted Vegetation Index (MSAVI) [51], and Soil Brightness [52]. Their methods extract both the intertidal flats and supratidal vegetated flats, but we focus on the unvegetated intertidal zone. Following Murray et al. (2019) [31], the indices used in this study include AWEI, NDWI, MNDWI, and NDVI. The four indices are expressed as Equations (1)–(4):

AWEI = ρblue + 2.5 × ρgreen − 1.5 × (ρnir + ρswir1) − 0.25 × ρswir2 (1)

ρgreen − ρnir NDWI = (2) ρgreen + ρnir

ρgreen − ρswir1 MNDWI = (3) ρgreen + ρswir1 ρ − ρ NDVI = nir red (4) ρnir + ρred

where ρblue, ρgreen, ρred, ρnir, ρswir1, and ρswir2 are the surface reflectance values of the corresponding bands of the Landsat images. In addition to the above indices, the Landsat near-infrared band (NIR) and Landsat short-wave infrared (SWIR) band are also considered. The Landsat images are categorized as stacks consisting of all images acquired within each year. For any cell with 30 meters’ spatial resolution, we can get the results by computing AWEI, NDWI, and MNDWI from all cells at the same location on different images within the same stack. To get the spectral change pattern in a stack, we also compute the maximum, minimum, median, standard deviation, the 10th, 25th, 50th, 75th, and 90th percentiles, and the means of all inputs in the specified percentile ranges (0–10, 10–25, 10–90, 25–50, 25–75, 50–75, 75–90, and 90–100). However, for NDVI, NIR, and SWIR, we only calculate the means of interval between the 10th and 90th percentiles. At this point, there are 54 variables for each cell. In addition, another two variables are used. (1) The first is the ETOPO1 Global Relief Model, which is developed by the National Oceanic and Atmospheric Administration (NOAA) of the US. It is a global relief model of the Earth’s surface, which integrates land topography and ocean bathymetry [53]. (2) The second is the global surface water occurrence data. This dataset has generated the location and temporal distribution of surface water, as well as the statistics on the extent and change of these water surfaces with 30 meters’ spatial resolution based on a total of 3,066,102 tiles of Landsat 5, 7, and 8 images, which were acquired between 16 March 1984 and 10 October 2015 [54]. These two variables combining with the 54 statistical results for each cell form 56 predictor variables. A pre-computed and pre-classified (tidal flat, permanent water, and other) sample point dataset is used as a training dataset for the RF machine learning algorithm. After the classification and post-processing, we can get the binary images, in which the cells with the value of 1 correspond to the tidal flats and the cells with the value of 0 correspond to the non-tidal flats. To validate the result, we use the validation dataset via random stratified sampling [31]. As the tidal flat cell delineation is accomplished, the foundation for tidal flat object level has been laid.

2.2. Tidal Flat Object Delineation In the two-dimensional space, one cell may have up to eight neighboring cells (up, down, left, right, upper-left, upper-right, lower-left, and lower-right) [55]. For the tidal ﬂat ISPRS Int. J. Geo-Inf. 2021, 10, 61 5 of 23

cells satisfying this eight-neighboring definition, the component-labeling algorithm [56] can assemble them as a contiguous patch, which is a tidal flat object in the context of this study. In addition, we can define a threshold to discard those too small tidal flat objects that may potentially add unnecessary complexity to the further analysis [12]. The threshold selection should be with respect to the spatial resolution and the size of the study area. The objects generated from different domains, such as ocean eddies [11], convective storms [12], and urban heat islands [13], can be tracked and linked as time-series chains based on the relative positions between two objects on different time steps. These previous studies prove that the object modeling is the foundation of the lifecycle tracking. Specific to this study, the object delineation plays as the foundation of capturing the morphological dynamics of tidal flats, which is invisible in the conventional (cell-level) assessment.

2.3. Tidal Flat Lifecycle Tracking For the tidal flat lifecycle tracking, the strategy should always be determined by the characteristics of the specific subject of research [11]. For the tidal flat lifecycle tracking, the overlapping method is realized, which finds the overlapping region between the two objects at the two adjacent time steps, considering the area ratios of the overlapping region to the two objects; then, it determines whether the two objects can be associated or not. In our study, for a pair of tidal flat objects, α is the object at the previous time step, β is the object at the current time step, and γ is the overlapping region between α and β. The overlapping ratio, R, is formularized as:

Aγ Aγ R = + (5) Aα Aβ

where Aα, Aβ, and Aγ are the areas of α, β, and γ, respectively. The value of R should not be less than 0, which corresponds to no overlapping case, and it should not be greater than 2, which corresponds to the exactly overlapping case. A threshold is defined to determine whether the two objects can get linked or not. As α and β are linked, a predecessor– successor relationship is established, in which α is the predecessor and β is the successor. The predecessor–successor relationships links the tidal flat objects between consecutive images, which refers to the lifecycle in this study. For the objects within the derived lifecycle, the numbers of their predecessors and successors may vary, and the areas of each pair of predecessor and successor may be different. To categorize the dynamic activities of tidal flats, we firstly define six events according to the numbers of the predecessors and successors (Figure2). In addition, we further define five events based on the area comparison between the predecessor and successor (Figure3). The object(s) on the first, second, and third time step are represented as A(s) (in red), B(s) (in blue), and C (in yellow), respectively. The solid line means the actual-existed object, and the dashed line means the pseudo-object (Figure2). The six events according to the numbers of predecessors and successors include: • Appearance: when the object B has no predecessors (Figure2a); • Disappearance: when the object A has no successors (Figure2b); • Merger: when the object B has two or more predecessors (A1,A2 ...,An) (Figure2c); • Ssplitting: when the object A has two or more successors (B1,B2 ...,Bn) (Figure2d); • Continuation: when the predecessor A has exactly one successor B, and the successor B has exactly one predecessor A (Figure2e); and • Recovery: when a group of linked objects starts with a splitting and end with a merger (Figure2f). ISPRS Int. J. Geo-Inf. 2021, 10, 61 6 of 23

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FigureFigure 2. 2.The The illustration illustration of of the the six six events events based based on on the the numbers numbers of of predecessors predecessors and and successors: successors: (a ) (a)Figure appearance; 2. The illustration(b) disappearance; of the six (c )events merger; based (d) splitting; on the numbers (e) continuation; of predecessors and (f )and recovery. successors: appearance;(a) appearance; (b) disappearance; (b) disappearance; (c) merger; (c) merger; (d) splitting; (d) splitting; (e) continuation; (e) continuation; and (f) and recovery. (f) recovery. TheThe object(s) object(s) on on the the ﬁrst first time time step step and theand second the second time step,time thestep, overlapping the overlapping region(s), re- The object(s) on the first time step and the second time step, the overlapping re- andgion(s), the largest and the overlapping largest overlapping region are representedregion are represented as A(s), B(s), C(s),as A(s), and B(s), Cmax C(s),, respectively. and Cmax, gion(s), and the largest overlapping region are represented as A(s), B(s), C(s), and Cmax, Therespectively. ﬁve events The based five onevents the based area comparison on the area betweencomparison the between predecessor the predecessor and successor and respectively. The five events based on the area comparison between the predecessor and include:successor include: • successor include: • Expansion: in case of continuation, the B’s area is greater than or equal to 110% of the • ExpansionExpansion: in: casein case of continuationof continuation, the, the B’s B’s area area is greater is greater than than or equalor equal to 110%to 110% of theof the A’sA’s area area (Figure (Figure3a); 3a); • A’s area (Figure 3a); • StabilityStability: in: in case case of ofcontinuation continuation, the, the ratio ratio of theof the B’s areaB’s area to the to A’sthe area A’s isarea between is between 90% • Stability: in case of continuation, the ratio of the B’s area to the A’s area is between and90% 110% and (Figure110% (Figure3b); 3b); • 90% and 110% (Figure 3b); • ContractionContraction:: in in case case of ofcontinuation continuation, the, the B’s B’s area area is is smaller smaller than than or or equal equal to to 90% 90% of of the the • Contraction: in case of continuation, the B’s area is smaller than or equal to 90% of the A’sA’s area area (Figure (Figure3c); 3c); A’s area (Figure 3c); • Annexation: in case of merger, multiple A(s) {A1, A2…, An} and the B share multiple • Annexation: in case of merger, multiple A(s) {A1,A2 ... ,An} and the B share multiple • Annexation: in case of merger, multiple A(s) {A1, A2…, An} and the B share multiple C(s) {C1, C2…, Cn}. If the area of Cmax is greater than or equal to 65% of the B’s area, C(s) {C1,C2 ... ,Cn}. If the area of Cmax is greater than or equal to 65% of the B’s area, C(s) {C1, C2…, Cn}. If the area of Cmax is greater than or equal to 65% of the B’s area, itit is is an anannexation annexation(Figure (Figure3d); 3d); and and • it is an annexation (Figure 3d); and 1 2 n • SeparationSeparation:: in in case case of ofsplitting splitting, the, the A A and and multiple multiple B(s) B(s) {B1 ,B{B 2, ...B …,,B Bn}} share share multiple multiple • Separation: in case of splitting, the A and multiple B(s) {B1, B2…, Bn} share multiple C(s)C(s) {C {C1,C1, C22...…, ,CCn}.n }.If Ifthe the area area of of C Cmaxmax is isgreater greater than than or orequal equal to to50.01% 50.01% of ofthe the A’s A’s ar- C(s) {C1, C2…, Cn}. If the area of Cmax is greater than or equal to 50.01% of the A’s ar- area,ea, it it is is a a separationseparation (Figure(Figure 3e).3e). ea, it is a separation (Figure 3e).

Figure 3. The illustration of the five events based on the area comparison between the predecessor FigureandFigure successor: 3. The 3. The illustration ( aillustration) expansion; of theof (theb ﬁve) stability; five events events based(c) basedcontraction; on on the the area (aread) comparison annexation; comparison between and be tween(e) separation. the the predecessor predecessor and successor: (a) expansion; (b) stability; (c) contraction; (d) annexation; and (e) separation. and successor: (a) expansion; (b) stability; (c) contraction; (d) annexation; and (e) separation. To demonstrate the tidal flat lifecycle tracking procedure, three consecutive snap- To demonstrate the tidal flat lifecycle tracking procedure, three consecutive snapshots taken at different time steps (t1, t2, and t3) are provided as an example (Figure 4), whichshots form taken a three-dimensionalat different time steps space (t 1(X,, t2 ,Y, and T). tThere3) are areprovided a total asof threean example tidal flat (Figure objects 4), which form a three-dimensional space (X, Y, T). There are a total of three tidal flat objects ISPRS Int. J. Geo-Inf. 2021, 10, 61 7 of 23

To demonstrate the tidal flat lifecycle tracking procedure, three consecutive snapshots taken at different time steps (t1, t2, and t3) are provided as an example (Figure4), which ISPRS Int. J. Geo-Inf. 2021, 10, x FORform PEER REVIEW a three-dimensional space (X, Y, T). There are a total of three tidal flat objects7 of 23 (A1,C1, and E1) at t1, six tidal flat objects (A2,B1,C2,D1,E2, and E3) at t2, and four tidal flat objects (A1, C1, and E1) at t1, six tidal flat objects (A2, B1, C2, D1, E2, and E3) at t2, and four tidal flat (B2,B3,C3, and E4) at t3. For better reference, the locations of some tidal flat objects are objects (B2, B3, C3, and E4) at t3. For better reference, the locations of some tidal flat objects projected on the adjacent snapshots, which are marked with undertint colors and dashed are projected on the adjacent snapshots, which are marked with undertint colors and lines such as A ’ of A . dashed lines such1 as A11’ of A1.

Figure 4. A tidal flat lifecycle tracking example. Figure 4. A tidal ﬂat lifecycle tracking example.

As Figure 4 shows, A1’ is the projection of A1 at t2, which shares sufficient overlap- As Figure4 shows, A ’ is the projection of A at t , which shares sufficient overlapping ping area with A2; therefore,1 the predecessor–succe1 ssor2 relationship is established, areawhere with A1 Ais 2the; therefore, predecessor the and predecessor—successor A2 is the successor, and relationship a continuation is happens established, to them. where A1 isThe the other predecessor predecessor–successor and A2 is the successor,relationships and in athiscontinuation figure are happensall established to them. in the The other predecessor–successorsame way, which contribute relationships a total of in four this ti figuredal flat are lifecycles. all established All other in types the same of events way, which contributebased on the a total numbers of four of predecessors tidal flat lifecycles. and succ Allessors other are types also visible of events in this based figure: on theA2’ is numbers ofthe predecessors projection ofand A2 at successors t3, and we arecan alsofind visiblenothingin overlaps this figure: with it, A 2so’ is A the2 has projection no succes- of A2 at t3sors, and and we a candisappearance find nothing occurs. overlaps Similarly, with B0 is it, the so projection A2 has no of successors B1 at t1, and and we can a disappearance find nothing overlaps with it, so B1 has no predecessors and an appearance occurs. On the oth- occurs. Similarly, B0 is the projection of B1 at t1, and we can find nothing overlaps with it, er hand, B1’ is the projection of B1 at t3. It overlaps with B2 and B3, which indicates that B1 so B1 has no predecessors and an appearance occurs. On the other hand, B1’ is the projection has two successors, and a splitting comes out. C2’ and D1’ are the projections of C2 and D1 of B1 at t3. It overlaps with B2 and B3, which indicates that B1 has two successors, and a at t3, and both overlap with C3. It means C3 has two predecessors and a merger happens. splitting comes out. C2’ and D1’ are the projections of C2 and D1 at t3, and both overlap In the same way, we could find E2 and E3 are the successors of E1, and they are also the with C . It means C has two predecessors and a merger happens. In the same way, we predecessors3 of E4. These3 four objects form a recovery. could find E2 and E3 are the successors of E1, and they are also the predecessors of E4. These2.4. Graph four Representation objects form and a recovery Analysis. for Tidal Flat Lifecycles In graph theory, the graph is defined as “a representation of a set of points (the 2.4. Graph Representation and Analysis for Tidal Flat Lifecycles nodes) and of how they are joined up (the edges), and any metrical properties are irrelevant”In [57]. graph Since theory, the graph the graph is a concise is defined way to as deliver “a representation the information, of a it set has of been points widely (the nodes) andused of in how GIS theyto represent are joined the upevolutionary (the edges), procedures and any of metrical dynamic properties geographic are phenome- irrelevant” [57]. Sincena. Moreover, the graph the is agraph concise operators way to provide deliver an the effective information, way to itcapture has been the widelyevents auto- used in GIS tomatically represent and the exhaustively evolutionary from procedures a large amount of dynamic of tidal flat geographic lifecycles, phenomena.which also high- Moreover, the graph operators provide an effective way to capture the events automatically and ISPRS Int. J. Geo-Inf. 2021, 10, 61 8 of 23

exhaustively from a large amount of tidal flat lifecycles, which also highlights that the ISPRS Int. J. Geo-Inf. 2021, 10, x FOR PEER REVIEW 8 of 23 object and lifecycle levels could provide more diversified dynamic information than the ISPRS Int. J. Geo-Inf. 2021, 10, x FORcell PEERlights level. REVIEW that the object and lifecycle levels could provide more diversified dynamic infor-8 of 23 lightsmationIn that thisthan the study, the object cell the level. and tidal lifecycle flat objects levels are could represented provide more by their diversified centroids, dynamic and the infor- predecess- or–successormationIn thisthan study, the relationships cell the level. tidal flat are objects represented are represented by the directedby their centroids, edges from and the prede- predecessors tocessor–successor theIn successors. this study, relationships the The tidal centroids flat areobjects represent form are re theedpresented by node the setdirected by V(G)their edgescentroids, and thefrom and directed the the predeces- prede- edges form thecessor–successorsors set to E(G),the successors. which relationships constitute The centroids are the represent form directed theed node graphby the set GdirectedV(G) in two-dimensionaland edges the directed from the edges predeces- space form (X, Y). To demonstratethesors set to theE(G), successors. which how the constitute The graph centroids worksthe directed form in this the graph study,node G set in the V(G)two-dimensional information and the directed of space Figure edges (X,4 isY). form derived To as demonstrate how the graph works in this study, the information of Figure 4 is derived as graphsthe set andE(G), drawn which constitute in Figure 5the. In directed this figure, graph it G is in easy two-dimensional to read the predecessor–successor space (X, Y). To graphs and drawn in Figure 5. In this figure, it is easy to read the predecessor–successor relationshipsdemonstrate how and the the graph corresponding works in this six study, events the based information on the of numbers Figure 4 of is predecessorsderived as and graphsrelationships and drawn and the in Figurecorresponding 5. In this sixfigure, events it is based easy to on read the thenumbers predecessor–successor of predecessors successorsand successors including includingappearance appearance, disappearance, disappearance, splitting, splitting,,merger merger,, continuationcontinuation, ,and and re-recovery. relationshipsAs the graphs and the are corresponding derived, the six information events based of on each the individualnumbers of tidalpredecessors flat lifecycle is coveryand successors. including appearance, disappearance, splitting, merger, continuation, and re- collected, organized, and stored in a spatiotemporal database simultaneously (Figure6). coveryAs. the graphs are derived, the information of each individual tidal flat lifecycle is This database can help us better understand and query the morphological changes between collected,As the organized, graphs are and derived, stored inthe a informationspatiotemporal of each database individual simultaneously tidal flat lifecycle(Figure 6). is thecollected,This adjacent database organized, tidal can flathelp and objects, us stored better which in understan a spatiotemporal are invisibled and query database in Figure the morphological simultaneously5, because tidal changes (Figure flat objects be-6). are representedThistween database the adjacent by can their helptidal centroids usflat better objects, as understan the which nodes. ared Especially,and invisible query in the theFigure morphological five 5, events because defined tidalchanges flat in Sectionob-be- 2.3 aretweenjects determined are the represented adjacent by tidal the by areaflattheir objects, comparisoncentroids which as thare betweene nodes.invisible theEspecially, in predecessor Figure the5, because five and events successor,tidal definedflat ob- and this spatiotemporalinjects Section are represented 2.3 are database determined by their can centroidsby help the area us as capture comparison the nodes. these Especially,between events. the the Moreover, predecessor five events for and defined the suc- six events basedincessor, Section on and the 2.3 thisnumber are spatiotemporal determined of predecessors by database the area and can comparison successors,help us capture between we introducethese the events.predecessor two Moreover, graph and operatorssuc- for to the six events based on the number of predecessors and successors, we introduce two assistcessor, the and analysis this spatiotemporal for tidal flat database dynamic can activities. help us capture these events. Moreover, for thegraph six operators events based to assist on the analysisnumber forof predecessorstidal flat dynamic and successors,activities. we introduce two graph operators to assist the analysis for tidal flat dynamic activities.

FigureFigure 5.5. The four four lifecycle lifecycle graphs graphs derived derived from from Figure Figure 4. 4. Figure 5. The four lifecycle graphs derived from Figure 4.

Figure 6. The structure of the spatiotemporal database. FigureFigure 6.6. The structure structure of of the the spatiotemporal spatiotemporal database. database. ISPRS Int. J. Geo-Inf. 2021, 10, 61 9 of 23

2.4.1. Use Degree to Identify Five Events For any node in a graph, its degree is defined as the number of edges connected to this node. For a directed graph, the degree of the node is further divided into indegree and outdegree. The indegree is defined as the number of arrows ending at this node, and the outdegree is defined as the number of arrows starting from this node [57]. In the context of this study, for any object, the indegree of its corresponding node equals the number of its predecessors, and the outdegree of its corresponding node equals the number of its successors. Based on this perception, the five events based on the numbers of predecessors and successors correspond to the following cases: • Appearance: when the node’s indegree is 0; • Disappearance: when the node’s outdegree is 0; • Splitting: when the node’s outdegree is greater than or equal to 2; • Merger: when the node’s indegree is greater than or equal to 2; and • Continuation: when the successor node’s indegree is 1 and the predecessor node’s outdegree is also 1.

2.4.2. Find Non-Cut Vertex to Identify the Event of Recovery A node v of a graph G is a cut vertex if the removal of v increases the number of components of G [57]. Apparently, the starting and ending nodes, which correspond to appearance and disappearance, are not the cut vertices. For the tidal flat lifecycles, which do not include the event of recovery, the rest nodes must be cut vertices. On the other hand, according to the definition of recovery, there are at least two parallel paths that exist between (but do not include) the splitting and merger nodes, and the nodes on the parallel paths are not the cut vertices. Therefore, we can justify whether a tidal flat lifecycle includes the recovery event or not by calculating the number of non-cut vertices other than the starting and ending nodes. If the number equals to zero, the tidal flat lifecycle has no recovery event; if the number is positive, the tidal flat lifecycle must include at least one recovery event.

3. A Case Study 3.1. Data and Study Area The following datasets are used in this case study: (1) Landsat TM (Thematic Mapper), ETM+ (Enhanced Thematic Mapper Plus), OLI (Operational Land Imager), and TIRS (Thermal Infrared Sensor) acquired by Landsat 4, 5, 7, and 8 during 1984 to 2018; (2) the ETOPO1 Global Relief Model; (3) the global surface water occurrence data; (4) the training point dataset; and (5) the validation point dataset. We use Google Earth Engine (GEE), a high-performance cloud computing platform, to access and process the above datasets and implement the RF machine learning algorithm. More than thirty years of geospatial datasets have been stored in the GEE virtual archives, which occupy over twenty petabytes of space and are updated and expanded daily. These large datasets can be instantly invoked and processed by the high-performance computing resources [58]. The case study area is the southwest tip of Florida peninsula, which belongs to the counties of Collier, Monroe, and Miami-Dade. This area is located within the coverage of the Landsat tiles of Path=15 Row=42, Path=15 Row=43, and Path=16 Row=42 (Figure7). From 1984 to 2018, a total of 2906 tiles of Landsat 4, 5, 7, and 8 images in this area have been pre-processed to surface reflectance Tier 1, and all of them are used to delineate the tidal flat cells in this study. The annual distribution of available Landsat images is shown in Figure8. In this figure, we categorize these images by year and get a total of 35 annual stacks from 1984 to 2018, which include 14 tiles of Landsat 4 (LT4) images, 1351 tiles of Landsat 5 (LT5) images, 1177 tiles of Landsat 7 (LE7) images, and 364 tiles of Landsat 8 (LC8) images. ISPRS Int. J. Geo-Inf. 2021, 10, 61 10 of 23

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FigureFigure 7.7. The casecase studystudy areaarea andand thethe coveragecoverage ofof LandsatLandsat tiles.tiles. Figure 7. The case study area and the coverage of Landsat tiles.

Figure 8. Annual distribution of available Landsat images. Figure 8. Annual distribution of available Landsat images. Figure 8. Annual distribution of available Landsat images. A major part of this study area belongs to Everglades National Park. Geologically, A majorthe part studyA of major this area study part is divided of area this belongs study into two area to subprovinc belongsEverglades to Evergladeses: National the west Park. National coastal Geologically, area Park. belongs Geologically, to the Ten the the study areastudyThousand is divided area isIslands into divided two Subprovince, intosubprovinc two subprovinces: whilees: the the west south the coastal west coastal area coastal area belongs area belongs belongs to the to Tenthe to theSouthern Ten Thou- At- Thousand Islandssandlantic Islands Subprovince,Coastal Subprovince, Strip while Subprovi the while southnce. the The coastal south Ten area coastalThousand belongs area Islands belongsto the SubprovinceSouthern to the Southern At- is the Atlantic transi- lantic CoastalCoastaltional Strip areaSubprovi Strip between Subprovince.nce. theThe Everglades Ten The Thousand Ten Thousand and GulfIslands Islandsof Mexico,Subprovince Subprovince which is consists the is thetransi- transitionalof tidal flats, area la- tional area betweenbetweengoons, uninhabitedthe the Everglades Everglades islands, and and Gulf mangrove ofof Mexico,Mexico, forests, whichwhich and consists consists highly of ofproductive tidal tidal flats, flats, lagoons,estuaries. la- uninhab- As the goons, uninhabiteditedclimate islands, islands,changes, mangrove mangrove the sea forests, level forests, andfluctuates, and highly highly productivewhile productive the sediment estuaries. estuaries. grows As theAs and climatethe recedes changes, over climate changes,thetime. sea the Tidal level sea flat fluctuates,level is onefluctuates, of while the the productswhile sediment the duri sediment growsng this and procedure,grows recedes and over whichrecedes time. plays Tidalover an flat important is one of time. Tidal flattherole is products inone protecting of the during products the this inland procedure, duri areasng this from which procedure, the plays hurricane-caused an which important plays role floods an inimportant protecting and breaking the inlandwaves role in protectingareasin the the from Gulf inland theof Mexico. hurricane-caused areas from The theSouthern hurricane-caused floods Atlantic and breaking Coastal floods wavesStrip and breakingSubprovince in the Gulf waves ofconsists Mexico. of Thema- in the Gulf ofSouthernrine Mexico. limestone, Atlantic The Southern which Coastal is Atlanticcovered Strip Subprovince Coastalby thin sheetsStrip consists Subprovince of quartz of marine sand. consists limestone, The limestoneof whichma- isstops covered wa- rine limestone,ter which in the isEverglades covered by from thin flowing sheets ofinto quartz the Atlantic sand. The Ocean. limestone However, stops there wa- is a marshy ter in the Evergladeschannel systemfrom flowing across intothe limestonethe Atlantic ridge Ocean. (i.e., However, Taylor Slough), there is which a marshy drains the water channel system across the limestone ridge (i.e., Taylor Slough), which drains the water ISPRS Int. J. Geo-Inf. 2021, 10, 61 11 of 23

by thin sheets of quartz sand. The limestone stops water in the Everglades from flowing ISPRS Int. J. Geo-Inf. 2021, 10, x FOR PEERinto REVIEW the Atlantic Ocean. However, there is a marshy channel system across the limestone11 of 23 ridge (i.e., Taylor Slough), which drains the water in the Everglades into Florida Bay. As a inresult, the Everglades the mud is into deposited Florida along Bay. theAs coast,a result, and the the mud tidal is flatdeposited region isalong produced the coast, [59– and62]. theHence, tidal the flat study region area is hasproduced a sufficient [59–62]. tidal Henc flat region,e, the study and itarea is regarded has a sufficient as an ideal tidal place flat region,to implement and it is the regarded case study. as an ideal place to implement the case study.

3.2. Implementation As all available available Landsat Landsat images images are are found found and and sorted sorted by by year, year, the the case case study study can can be implementedbe implemented following following Figure Figure 9. The9. The Landsat Landsat images images acquired acquired at the at thesame same time time are aremo- saickedmosaicked and and clipped clipped according according to tothe the study study ar area’sea’s boundary. boundary. The The clouds, clouds, cloud cloud shadows, shadows, cirrus, and scan-line corrector (SLC)-off gaps are classiﬁedclassified as bad-quality observations. To identifyidentify andand eliminateeliminate them,them, thethe FunctionFunction of maskmask (FMask)(FMask) AlgorithmAlgorithm is used in thisthis study [63,64]. [63,64].

Figure 9. The general workflow workﬂow of the study.

Next, wewe useuse the the cells cells of of the the same same location location from from all mosaicked–denoised all mosaicked–denoised images images under underthe same the annualsame annual stack to stack calculate to calculate the 56 predictorthe 56 predictor variables variables as described as described in Section in Sec- 2.1. Astion the 2.1. predictor As the predictor variable variable calculation calculation and the and RF classifierthe RF classifier training training are finished, are finished, the RF theclassification RF classification could delineate could delineate the tidal the flat tidal cells andflat havecells and an overall have an accuracy overall of accuracy 95.23%. Inof 95.23%.the end, In every the annualend, every stack annual contributes stack onecont binaryributes image.one binary The followingimage. The processes following will pro- be cessesoperated will based be operated on this tidalbased flat on cell this delineation tidal flat cell result. delineation result. As shownshown in in Figure Figure9, the9, followingthe following processes processes are tidal are flat tidal object flat assemblage, object assemblage, lifecycle lifecycletracking, tracking, the graph-based the graph-based representation, representa and thetion, spatiotemporal and the spatiotemporal analysis of events.analysis Pro- of events.ceeding Proceeding with the work, with a prototypethe work, systema prototype is developed system via is MATLAB.developed Avia threshold MATLAB. value A thresholdof 3 is used value to discard of 3 is theused tidal to discard flat objects, the tidal which flat are objects, smaller which than are three smaller cells’ than coverage three cells’area(2700 coverage m2), area and the(2700 value m2), of and 0.6 the is used value as of the 0.6 overlapping is used as thresholdthe overlapping (Equation threshold (5)) to (Equationdetermine (5)) the to predecessor–successor determine the predecessor–successor relationship. relationship. Based on the implemental proceduresprocedures asas illustratedillustrated above,above,we we get get the the graphs graphs of of tidal tidalflat flat lifecycles, lifecycles, and and the the spatial, spatial, temporal, temporal, and morphologicand morphologic attributes attributes of tidal of flats tidal are flats stored are storedin the database.in the database. The graph The operatorsgraph operators are used are to used analyze to analyze the generated the generated graphs andgraphs get and get the spatiotemporal characteristics for each type of event. The results of this case study are shown and discussed in Section 3.3.

3.3. Results and Discussion 3.3.1. Characteristics of Tidal Flat Cells, Objects, and Lifecycles ISPRS Int. J. Geo-Inf. 2021, 10, 61 12 of 23

the spatiotemporal characteristics for each type of event. The results of this case study are shown and discussed in Section 3.3.

3.3. Results and Discussion ISPRS Int. J. Geo-Inf. 2021, 10, x FOR PEER REVIEW 12 of 23 3.3.1. Characteristics of Tidal Flat Cells, Objects, and Lifecycles AsAs described described in in Section Section 3.2 3.2,, we we get get a a total total of of 35 35 binary binary images, images, which which are are the the annual annual recordsrecords ofof thethe tidaltidal flat flat cells cells from from 1984 1984 to to 2018. 2018. ToTo demonstratedemonstrate thethe delineationdelineation result,result, FigureFigure 10 10shows shows the the annual annual tidal tidal flat flat area area from from 1984 1984 to to 2018. 2018. As As shown shown in in the the figure, figure, the the 2 maximummaximum record record (262.5 (262.5 km km) happened2) happened in 1992, in 1992, which which is significantly is significantly greater greater than the than mean the 2 2 annualmean annual area (129.39 area km(129.39) and km the2) secondand the maximum second maximum record (173.1 record km (173.1in 2008). km Hurricane2 in 2008). AndrewHurricane (Category Andrew 5) (Category may be used 5) may to explainbe used theto explain unusual the record unusual in 1992. record According in 1992. Ac- to Risicording et al. to(1995) Risi et [65 al.], (1995) this hurricane [65], this created hurricane a substantial created a substantia amount ofl amount sediment of depositssediment alongdeposits the along shallow the subtidal shallow coastline subtidal incoastline our study in our area. study area.

FigureFigure 10. 10.Annual Annual distribution distribution of of the the tidal tidal ﬂat flat area. area.

ToTo betterbetter understandunderstand the spatial distribution distribution of of the the delineated delineated tidal tidal flat flat cells, cells, we we ag- aggregategregate the the occurrences occurrences of of tidal tidal flat flat cells cells based on all 3535 binarybinary imagesimages (Figure(Figure 11 11).). As As shown,shown, thethe tidaltidal flatflat cells cells in in different different years years are are heavily heavily overlapped, overlapped, and and they they are are mostly mostly distributeddistributed in in four four regions: regions: (A)(A) the the northwestern northwestern corner corner of of the the study study area; area; (B) (B) the the middle middle portionportion ofof thethe west west coast; coast; (C)(C) the the turning turning pointpointbetween betweenthe thewest westcoast coastand andsouth southcoast; coast; andand (D) (D) the the eastern eastern portion portion of of the the south south coast. coast. Region Region A A belongs belongs to to Rookery Rookery Bay Bay National Nation- Estuarineal Estuarine Research Research Reserve, Reserve, which which is oneis on ofe of the the few few remaining remaining undisturbed undisturbed mangrove mangrove estuaryestuary system system in in North North America. America. This This system system plays plays an importantan important role role for thefor resuspensionthe resuspen- andsion transportation and transportation of sediments of sediments and contributes and contributes to an advantageous to an advantageous environment environment for tidal flatfor dynamictidal flat dynamic activities activities [66]. Similarly, [66]. Similarly, region C region is located C is aroundlocated Capearound Sable, Cape in Sable, which in awhich man-made a man-made canal project canal was project carried was out carried in 1922. out This in project1922. hasThis largely project changed has largely the hydrologicchanged the environment hydrologic of environment the surrounding of the area. surro Asunding a result, area. an activeAs a result, tidal flat an regionactive wastidal producedflat region around was produced Sandy Key around Basin, Sandy which Key is locatedBasin, which off the is coast located of Capeoff the Sable coast [67 of]. Cape On theSable other [67]. hand, On regionthe other B is locatedhand, region around B the is estuarylocated ofaround Shark the River estuary Slough, of while Shark region River DSlough, is located while around region the D estuary is located of Taylor around Slough. the estuary These twoof Taylor rivers Slou are thegh. principalThese two natural rivers drainagesare the principal that keep natural carrying drainages the overland that keep water carrying and mud the fromoverland the Everglades water and tomud the from sea. Consequently,the Everglades the to tidal the flatsea. regionsConsequently, are intensive the tidal in the flat two regions places. are intensive in the two places. ISPRS Int. J. Geo-Inf. 2021, 10, 61 13 of 23

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Figure 11. SpatialFigure distributions 11. Spatial of totaldistributions tidal flat occurrences of total tidal from flat 1984 occu torrences 2018. A, from B, C, 1984 and to D are2018. four A, tidal B, C, flat and intensive D are regions. four tidal flat intensive regions. By comparing the 35 cell level maps, we can summarize the area changes of tidal flats By comparingin the above the 35 four cell regions level maps, and the we rest can of summarize the region, asthe shown area changes in Figure of 12 tidal. Region A has 2 flats in the abovethe smallest four regions annual and average the rest area of amongthe region, thefour as shown regions in (8.22Figure km 12.). Region The tidal A flat area in has the smallestthis region annual periodically average area changes, among asthe it reachesfour regions the peak (8.22 every km2). eleven The tidal years flat (1986, ar- 1997, and 2 ea in this region2008). periodically The maximum changes, area happened as it reaches in 1997, the which peak isevery 19.62 eleven km , whileyears the (1986, minimum area 2 1997, and 2008).happened The inmaximum 2016, which area is onlyhappened 1.87 km in .1997, Region which B has is the 19.62 second km smallest2, while annualthe average 2 minimum areaarea amonghappened the fourin 2016, regions which (18.04 is kmonly). 1.87 Its tidal km2 flat. Region area change B has alsothe hassecond regularity, as it smallest annualreaches average the peak area every among five tothe seven four years regions (1986, (18.04 1991, km 1997,2). 2002,Its tidal 2008, flat and area 2015). Similar 2 change alsoto has region regularity, A, the maximumas it reaches tidal the flat peak area every of region five to B seven happened years in (1986, 1997 (32.751991, km ), and 2 1997, 2002, the2008, minimum and 2015). tidal Similar flat areato region (7.78 A, km the) happenedmaximum intidal 2017, flat which area of is region only one B year after that of region A. Region C has the second largest annual average area among the four happened in 1997 (32.75 km2), and the minimum tidal flat area (7.78 km2) happened in regions (42.28 km2). Although the regularity of the tidal flat area change is not significant, 2017, which is only one year after that of region A. Region C has the second largest an- it is obvious that two peaks exist in 1989 and 2009, while there is a valley between these nual average area among the four regions (42.28 km2). Although the regularity of the two peaks. The maximum area (72.61 km2) happened in 2009, while the minimum area tidal flat area change is not significant, it is obvious that two peaks exist in 1989 and (8.84 km2) happened in 1995. Region D has the largest annual average area among the four 2009, while there is a valley between these two peaks. The maximum area (72.61 km2) regions (51.43 km2). Similar to region C, its tidal flat area change does not have significant happened in 2009, while the minimum area (8.84 km2) happened in 1995. Region D has regularity. The maximum area (100.3 km2) happened in 1992, while the minimum area the largest annual average area among the four regions (51.43 km2). Similar to region C, (14.96 km2) happened in 1989. The annual average area of the tidal flat in the rest of the its tidal flat area change does not have significant regularity. The maximum area (100.3 region is 9.42 km2, which is only 7.3% of the annual average area of the tidal flat in the km2) happened in 1992, while the minimum area (14.96 km2) happened in 1989. The an- whole study area. It further proves that the tidal flat cells in the study area are mainly nual average area of the tidal flat in the rest of the region is 9.42 km2, which is only 7.3% distributed in the four intensive regions. of the annual average area of the tidal flat in the whole study area. It further proves that the tidal flat cells in the study area are mainly distributed in the four intensive regions. ISPRS Int. J. Geo-Inf. 2021, 10, 61 14 of 23

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FigureFigure 12. 12. Annual Annual distributionFigure distribution 12. Annual of of tidal tidal distributionflat flat area area by by ofregions. regions. tidal ﬂat area by regions.

BasedBased on on the the delineated Baseddelineated on thetidal tidal delineated flat flat cells, cells, a tidala total total flatof of 3672 cells,3672 objects objects a total are are of assembled, 3672assembled, objects and and are 962 962 assembled, and tidaltidal flat flat lifecycles lifecycles962 tidalare are tracked flattracked lifecycles from from the arethe st trackedstudyudy area. area. from Every Every the tidal studytidal flat flat area. lifecycle lifecycle Every has has tidal a a dura- flatdura- lifecycle has a tiontion of of at at least leastduration one one year. year. of Figure Figure at least 13 13 one shows shows year. the the Figure duration duration 13 shows distribution distribution the duration of of these these distribution 962 962 tidal tidal flat offlat these 962 tidal lifecycleslifecycles and and flatgives gives lifecycles the the mean mean and duration duration gives the of of mean 2.8 2.8 years. years. duration As As shown ofshown 2.8 years. in in the the As figure, figure, shown a a intotal total the of figure,of 476 476 a total of 476 tidaltidal flatflat lifecycles lifecyclestidal flat havehave lifecycles thethe durationduration have the of of duration only only oneone of year, onlyyear, one whichwhich year, are are which nearlynearly are halfnearlyhalf ofof half thethe of the tracked trackedtracked tidal tidal flat tidalflat lifecycles. lifecycles. flat lifecycles. There There are Thereare 863 863 are tidal tidal 863 flat flat tidal lifecycles lifecycles flat lifecycles that that have have that 5 5 years haveyears or 5 or years less less du- ordu- less duration, ration,ration, while while onlywhile only 99 only99 tidal tidal 99 tidalflat flat lifecycles flatlifecycles lifecycles live live livelonger longer longer than than than five five five years. years. years. It ItIt is is obvious obviousobvious that that most tidal flat mostmost tidal tidal flat flat lifecycleslifecycles lifecycles havehave have shortshort short durations.durations. durations.

FigureFigure 13. 13. The The duration Figureduration 13. distribution distributionThe duration of of the distributionthe 962 962 tidal tidal flatof flat the lifecycles. lifecycles. 962 tidal ﬂat lifecycles.

ToTo demonstrate demonstrateTo the demonstratethe graph graph representati representati the graphonon representation and and analysis, analysis, andwe we select analysis,select the the we178 178 selectthth tidal tidal the flat flat 178th tidal flat lifecyclelifecycle as as an an lifecycleexample, example, as and and an example,it it is is illustrated illustrated and it in isin Figure illustratedFigure 14. 14. This inThis Figure tidal tidal flat14 flat. Thislifecycle lifecycle tidal lasts lasts flat lifecyclesix six lasts six yearsyears (1984–1990) (1984–1990)years and and (1984–1990) includes includes a anda variety variety includes types types a of varietyof events, events, types so so it ofit is is events, considered considered so it isas as consideredan an ideal ideal as an ideal casecase to to be be analyzed. analyzed.case to The The be analyzed.location location of of Thethis this locationtida tidal lflat flat of lifecycle lifecycle this tidal within within flat lifecyclethe the whole whole within study study the area area whole is is study area shownshown in in Figure Figureis shown 14a, 14a, which which in Figure is is in in14 region a,region which D. D. isAs As in described described region D. in Asin Figure Figure described 6, 6, the the in starting Figurestarting6 ,and theand starting and endingending coordinates coordinatesending of of coordinateseach each tidal tidal flat offlat eachlifecycle lifecycle tidal are are flat stored stored lifecycle in in the arethe spatiotemporal storedspatiotemporal in the spatiotemporal database, database, database, soso we we can can easily easilyso query we query can the the easily location location query of of theany any locationsp specifiedecified of tidal tidal any flat specifiedflat lifecy lifecycle.cle. tidal Moreover, Moreover, flat lifecycle. the the lo- lo- Moreover, the cationscations of of the thelocations tidal tidal flat flat ofobjects objects the tidal (represent (represent flat objectseded by by (represented their their centroids centroids by theiras as the the centroids nodes) nodes) asand and the the the nodes) and the movementmovement track track (represented (represented as as the the edges) edges) with withinin this this lifecycle lifecycle are are shown shown in in Figure Figure 14b, 14b, whichwhich are are also also queried queried from from the the spatiotemporal spatiotemporal database. database. The The derived derived graph graph of of Figure Figure ISPRS Int. J. Geo-Inf. 2021, 10, 61 15 of 23

movement track (represented as the edges) within this lifecycle are shown in Figure 14b, which are also queried from the spatiotemporal database. The derived graph of Figure 14b ISPRS Int. J. Geo-Inf. 2021, 10, xis FOR drawn PEER REVIEW in Figure 14c. Compared with Figure 14b, the derived graph in Figure15 of 23 14c is a succinct version to illustrate the predecessor–successor relationships within the 178th tidal 14b is drawn in Figure 14c. Compared with Figure 14b, the derived graph in Figure 14c flat lifecycle. By calculating the indegree and outdegree of each object and querying the is a succinct version to illustrate the predecessor–successor relationships within the 178th areastidal of eachflat lifecycle. tidal flat By objectcalculating from the the indegree spatiotemporal and outdegree database, of each object we can and easily querying summarize the eventthe areas types of each existing tidal flat in thisobject lifecycle, from the whichspatiotemporal include database,appearance we, cancontinuation easily sum-(stability), splittingmarize(separation the event), typesmerger existing(annexation in this lifecycle,), recovery which, and includedisappearance appearance., Thecontinuation corresponding event(stability information), splitting is (separation also stored), merger in the (annexation database.), recovery This graph, and disappearance representation. The corre- and analysis providesponding a straightforward event information way is toalso examine stored in the the dynamicdatabase. This activities graph withinrepresentation an individual and tidal analysis provide a straightforward way to examine the dynamic activities within an in- flat lifecycle. dividual tidal flat lifecycle.

Figure 14. The lifecycle of the 178th tidal flat. (a) The centroid location of this lifecycle within the Figure 14. The lifecycle of the 178th tidal ﬂat. (a) The centroid location of this lifecycle within the study area; (b) the actual study area; (b) the actual movement of tidal flat objects within this lifecycle; and (c) the graph rep- movement of tidal ﬂat objects withinresentation this lifecycle; for this lifecycle. and (c ) the graph representation for this lifecycle.

As theAs the first first effort effort ofof the the multi-level multi-level GIS GISframework framework on the issue on the of tidal issue flats, of we tidal ex- flats, we examineamine the the dynamic dynamic activities activities of oftidal tidal flats flats from from a novel a novel perspective. perspective. The temporal The temporal and and spatialspatial characteristics characteristics of of tidaltidal flat flat events events ar aree presented presented in the in below the below sections. sections. Since the Since the casescases of the of thecontinuation continuationevent event areare fully covered covered by by three three types types of events of events based basedon the ar- on the area ea comparison between the predecessor and successor (contraction, stability, and expan- contraction stability expansion comparisonsion), the below between sections the do predecessor not discuss the and continuation successor event. ( , , and ), the below sections do not discuss the continuation event. 3.3.2. Temporal Characteristics of Tidal Flat Events 3.3.2. Temporal Characteristics of Tidal Flat Events Annual numbers of appearance, disappearance, splitting, merger, and recovery are drawn in Figures 15 and 16. For better reference, the descriptive statistics derived from these ISPRS Int. J. Geo-Inf. 2021, 10, x FOR PEER REVIEW 16 of 23 ISPRS Int. J. Geo-Inf.ISPRS 2021 Int., 10 J., x Geo-Inf. FOR PEER2021 ,REVIEW10, 61 16 of 23 16 of 23 Annual numbers of appearance, disappearance, splitting, merger, and recovery are drawnAnnual in Figures numbers 15 and of 16. appearance For better, disappearance reference, the, splitting descriptive, merger statistics, and derivedrecovery from are drawn in Figures 15 and 16. For better reference, the descriptive statistics derived from these two figurestwo figuresis shown is in shown Table in 1. Table As shown1. As in shown Figure in 15, Figure the initial15, the year initial (1984) year in (1984) in this these two figures is shown in Table 1. As shown in Figure 15, the initial year (1984) in this case studycase has study the hashistorical the historical maximum maximum record recordof appearance of appearance (104).(104). Apparently, Apparently, it it could be this case study has the historical maximum record of appearance (104). Apparently, it could be overestimated,overestimated, because because we wedo donot not have have available available data data before before 1984. 1984. Similarly, Similarly, the tidal flat could be overestimated, because we do not have available data before 1984. Similarly, the tidal flat lifecycleslifecycles that that end end in in the the final final year year (2018) (2018) may may exist exist longer longer and and disappear disappear in in the future the tidal flat lifecycles that end in the final year (2018) may exist longer and disappear in the future years.years. To Toeliminate eliminate the the interference, interference, these these two two years’ years’ records records of of appearanceappearance andand disappearance the future years. To eliminate the interference, these two years’ records of appearance and disappearance areare both both ignored ignored in the statistics in Table 1 1.. disappearance are both ignored in the statistics in Table 1. From Figure 15,From we Figurecan see 15 that, we the can annual see that occurrence the annual of occurrence appearance ofis moreappearance stableis more stable than Fromthat of Figure disappearancethan 15, that we of ,can disappearanceand see this that preliminary the, and annual this observation preliminary occurrence can observationof beappearance verified can isin more beTable verified stable 1. As in Table1. As thanthe smaller that of valuedisappearancethe smaller of standard, value and thisdeviation of standard preliminary implies deviation observation higher implies stability, can higher be the verified stability, event in of the Table appearance event 1. ofAsappearance has thehas smallerthe smaller valuethe standard smaller of standard standarddeviation deviation deviation (σ = 7.76) implies ( σthan= 7.76)higher that than of stability, disappearance that of thedisappearance event (σ = 9.93).of appearance(σ In=9.93). par- In particular, hasticular, the smallerthere therewere standard were60 disappearance 60deviationdisappearance (σevents = 7.76)events in than 2004 in that 2004and of and46 disappearance disappearance 46 disappearance (σ events= 9.93).events in In 2011, inpar- 2011, while the ticular,while the there mean meanwere value value60 disappearanceof ofannual annual occurrence occurrenceevents in of 2004 of disappearancedisappearance and 46 disappearance isis 27.51. 27.51. We We events cancan concludeconclude in 2011, that the years whilethat the the years mean ofof 20042004value andand of 20112011annual haveha veoccurrence unusuallyunusually of higherhigher disappearance occurrencesoccurrences is 27.51. ofofdisappearance disappearance We can conclude.. that the years of 2004 and 2011 have unusually higher occurrences of disappearance.

Figure 15.Figure Annual 15. distributionAnnual distribution of the events of the of events appearance of appearance and disappearance. and disappearance. Figure 15. Annual distribution of the events of appearance and disappearance.

Figure 16. Annual distribution of the events of splitting, merger, and recovery. Figure 16.Figure Annual 16. distributionAnnual distribution of the events of the of eventssplitting, of splitting,merger, and merger, recovery. and recovery. In Table 1, weIn can Table also1 ,find we the can annual also find mean the occurrences annual mean of occurrencessplitting, merger of ,splitting and re-, merger, and coveryIn are Table 16.09, 1, we 15.12, can andalso 2.50.find theBased annual on these mean mean occurrences values, weof splitting can find, merger a total, andof five recovery are 16.09,recovery 15.12,are and 16.09, 2.50. 15.12, Based and on 2.50.these Based mean onvalues, these we mean can values, find a wetotal can of findfive a total of five peaks with significantlypeaks with higher significantly event highernumbers event than numbers the corresponding than the corresponding mean values meanin values in peaksFigure with16, which significantly are in the higher years eventof 1988, numbers 1996, 2004, than 2011, the correspondingand 2015. Aside mean from values the peak in Figure 16, whichFigure are 16in ,the which years are of in 1988, the years1996, of2004, 1988, 2011, 1996, and 2004, 2015. 2011, Aside and from 2015. the Aside peak from the peak ISPRS Int. J. Geo-Inf. 2021, 10, 61 17 of 23

ISPRS Int. J. Geo-Inf. 2021, 10, x FOR PEER REVIEW in 2015, the other peaks imply a persistent and stable periodicity of about17 of 23 seven to eight years. in 2015, the other peaks imply a persistent and stable periodicity of about seven to eight years. Table 1. The descriptive statistics of the events.

σ Event TypeTable Maximum 1. The descriptive statistics of Minimum the events. Mean (x) Standard Deviation ( ) Appearance 43 (1991) 11 (2016) 26.03 7.76 Standard Deviation Event Type Maximum Minimum Mean (x)̄ Disappearance 60 (2004) 11 (2016) 28.36(σ) 9.93 Appearance Splitting 43 (1991) 43 (2004) 11 (2016) 2 (2016) 26.03 16.09 7.76 7.59 Disappearance Merger 60 (2004) 40 (2004) 11 (2016) 5 (2018) 28.36 15.12 9.93 6.82 Splitting 43 (2004) 2 (2016) 16.09 7.59 0 (1985–1986, 1990–1992, 1995, Merger Recovery 40 (2004) 10 (1988, 2014, 2015) 5 (2018)1997, 1999, 2001–2002, 2006, 15.12 2.50 6.82 3.23 10 (1988, 2014, 0 (1985–1986, 1990–1992, 1995,2008–2010, 1997, 1999, 2014, 2001–2002, 2016, 2018) Recovery 2.50 3.23 Contraction2015) 44 (1987)2006, 2008–2010, 2014, 2016, 10 2018) (2015) 29.32 8.68 Contraction 44 (1987) 10 (2015) 29.32 8.68 Stability 42 (2004) 1 (2018) 17.44 7.48 Stability 42 (2004) 1 (2018) 17.44 7.48 Expansion Expansion 55 (1986) 55 (1986) 5 (2016) 5 (2016) 25.91 25.91 11.37 11.37 Annexation Annexation 10 (2003) 10 (2003) 0 (2015) 0 (2015) 3.91 3.91 2.53 2.53 Separation Separation24 (1985) 24 (1985)0 (2015-2018) 0 (2015-2018)5.03 5.034.53 4.53 Note: The correspondingNote: The years corresponding are labeled years in the are labeledround brackets. in the round brackets.

The annual numberThe annual and numberdescriptive and statisti descriptivecs for statistics the events for based the events on the based area on com- the area compar- parison betweenison the between predecessor the predecessor and successor and successorare shown are in shownFigures in 17 Figures and 18, 17 and and Ta- 18, and Table1 . ble 1. Figure Figure17 and 17Table and 1 Table indicate1 indicate that occurrences that occurrences of stability of stability (x ̄ = 17.44)(x = 17.44)are less are than less than occur- occurrences ofrences expansion of expansion (x ̄ = 25.91)(x = and 25.91) contraction and contraction (x ̄ = 29.32),(x = which 29.32), further which furtherreveals revealsthat that tidal tidal flat objectsﬂat tend objects to have tend tosignificant have signiﬁcant area changes area changesrather than rather keeping than keepingsteady. How- steady. However, ever, there werethere 42 were stability 42 stability events inevents 2004 inand 2004 31 andstability 31 stability events inevents 2000, in which 2000, are which dis- are distinctly tinctly largerlarger than the than mean the mean value value of annual of annual stabilitystability events.events. On the On other the other hand, hand, there there were 34 were 34 contractioncontraction eventsevents and and28 expansion 28 expansion eventsevents in 2004, in 2004, and and there there were were 17 17 contractioncontraction events and events and 2424 expansionexpansion eventsevents in in 2000, 2000, which which are are less less than than the the event event numbers numbers of of stabilitystability in these two in these two years. These These comparisons comparisons demonstrate demonstrate that that the the years years of of 2000 2000 and and 2004 2004 have have unusually more stability events. unusually more stability events.

FigureFigure 17. Annual 17. Annual distribution distribution of the ofevents the events of expansion, of expansion, stability, stability, and contraction. and contraction.

From Figure 18, we can find the periods when most separation events occurred are (1) from 1985 to 1993; (2) from 2001 to 2002; (3) from 2004 to 2006; and (4) from 2008 to 2010. A total of 125 separation events happened during these four periods, and the annual mean occurrence of the separation event during these four periods is 7.35, which is significantly larger than the overall annual mean of the event occurrence of separation (x ̄ = 5.03). The periods when most of the annexation events occurred are (1) from 1994 to 2000; ISPRS Int. J. Geo-Inf. 2021, 10, x FOR PEER REVIEW 18 of 23

(2) the year of 2003; (3) the year of 2007; and (4) from 2011 to 2018. A total of 75 annexation events happened during these periods, and the annual mean of the event occurrence of annexation is 4.41, which is significantly larger than the overall annual mean of the event occurrence of annexation (x ̄ = 3.91). In addition to the above discoveries, another interesting finding is that the year of 2004 is a unique year. As suggested in Section 3.3.1, the hurricane activities may be the ISPRS Int. J. Geo-Inf. reason.2021, 10, 61There have been three hurricanes that affected the study area in that year, which 18 of 23 are Hurricane Charley (Category 4), Hurricane Frances (Category 4), and Hurricane Ivan (Category 5) [68].

Figure 18. FigureAnnual 18. distributionAnnual distribution of the events of theof annexation events of annexation and separation. and separation.

3.3.3. Spatial CharacteristicsFrom Figure of Tidal18, we Flat can Events find the periods when most separation events occurred are (1) from 1985 to 1993; (2) from 2001 to 2002; (3) from 2004 to 2006; and (4) from 2008 to 2010. A In addition to the temporal characteristics, the spatial characteristics of each type of total of 125 separation events happened during these four periods, and the annual mean event can be revealed by plotting the event occurrence places as points on the map. occurrence of the separation event during these four periods is 7.35, which is significantly These event occurrence places are determined by the definitions of the specified types of larger than the overall annual mean of the event occurrence of separation (x = 5.03). The events. In detail, the disappearance, splitting, and separation events are only defined by the periods when most of the annexation events occurred are (1) from 1994 to 2000; (2) the year number of successors. Therefore, each event must have exactly one predecessor node, of 2003; (3) the year of 2007; and (4) from 2011 to 2018. A total of 75 annexation events which is regarded as the event occurrence place. However, for the rest event types, the happened during these periods, and the annual mean of the event occurrence of annexation terminal node is unique (i.e., the successor node of appearance, continuation, contraction, is 4.41, which is significantly larger than the overall annual mean of the event occurrence stability, expansion, merger, annexation, and the successor node of the merger is part of re- of annexation (x = 3.91). covery). So, the occurrence places of these events are determined as the terminal node. In addition to the above discoveries, another interesting finding is that the year of For better visualization,2004 is a unique a fishnet year. is Ascreated suggested via ArcGIS in Section to cover 3.3.1 the, the whole hurricane study activitiesarea, in may be the 2 which the sizereason. of each There grid is have 4 km been. The three number hurricanes of event that occurrences affected the is studycounted area for in each that year, which grid, and the aregrid-based Hurricane summary Charley maps (Category for all 4), types Hurricane of events Frances are drawn (Category in Figure 4), and 19. Hurricane Ivan The preliminary(Category observation 5) [68 ].of Figure 19 suggests that these event types can be classified as four categories according to their occurrences, in which the events of appearance (Figure 19a), 3.3.3.disappearance SpatialCharacteristics (Figure 19b), contraction of Tidal Flat (Figure Events 19c), and expansion (Figure 19e) belong to the Inhighest addition occurrence to the temporal category, characteristics, the events of stability the spatial (Figure characteristics 19d), merger of each type of (Figure 19f), andevent splitting can be (Figure revealed 19h) by plottingbelong to the the event seco occurrencend highest placesoccurrence as points category, on the map. These the events of eventannexation occurrence (Figure places 19g) and are determinedseparation (Figure by the 19i) definitions belong ofto thethe specifiedthird high- types of events. est occurrenceIn category, detail, the anddisappearance the event ,ofsplitting recovery, and (Figureseparation 19j) belongsevents are to onlythe lowest defined oc- by the number currence category.of successors. This finding Therefore, is consistent each event with mustthe comparison have exactly of onethe predecessorannual means node, which is (x)̄ in Table 1regarded and it is easy as the to event understand, occurrence because place. the However, event of forannexation the rest is event the special types, the terminal case of mergernode, event is of unique separation (i.e., is the the successor special case node of ofsplittingappearance, and, thecontinuation, event of recovery contraction , stability, is the special caseexpansion of both, merger merger, annexation and splitting, and. the successor node of the merger is part of recovery). So, Accordingthe to occurrence Section 3.3.1, places the oftidal these flat eventscells are are intensively determined distributed as the terminal in four node. re- For better gions within visualization,the study area, a fishnetwhich isshould created potentially via ArcGIS affect to cover the spatial the whole distribution study area, of in which the each event. Thissize is of proved each grid with is Figure 4 km2 .19, The as numberall types of of event events occurrences are mainly isdistributed counted for each grid, within or aroundand the four grid-based regions. summary Accordingl mapsy, the for occurrence all types ofplaces events of these are drawn events in are Figure 19. The preliminary observation of Figure 19 suggests that these event types can be classified as four categories according to their occurrences, in which the events of appearance (Figure 19a), disappearance (Figure 19b), contraction (Figure 19c), and expansion (Figure 19e) belong to the highest occurrence category, the events of stability (Figure 19d), merger (Figure 19f), and splitting (Figure 19h) belong to the second highest occurrence category, the events of annexation (Figure 19g) and separation (Figure 19i) belong to the third highest occurrence category, and the event of recovery (Figure 19j) belongs to the lowest occurrence category. ISPRS Int. J. Geo-Inf. 2021, 10, x FOR PEER REVIEW 19 of 23

counted by five classes (the four intensive regions and the rest region), and the summary is drawn in Figure 20. This figure shows that 89.47% of annexation events and 75.44% of separation events are distributed within the four regions, which are the highest and lowest records among all types of events and verify the above finding. Another interesting finding is that even though the tidal flat area in region C is not the largest among the four regions, a total of eight types of events (appearance, disappearance, contraction, expansion, merger, annexation, splitting, and separation) are more in region C than elsewhere.

ISPRS Int. J. Geo-Inf. 2021, 10, 61 Similarly, although the tidal flat area in region A is the smallest among the four regions,19 of 23 the number of event occurrences in region A is ranked as the third most among the four regions in cases of appearance, disappearance, and stability events and the second most among the four regions in cases of the rest event types. Therefore, we can conclude that theThis tidal ﬁnding flat dynamic is consistent activities withthe are comparison more frequent of the in regions annual meansA and (Cx )than in Table regions1 and B itand is D.easy This to understand,may be explained because by theregarding event of theannexation fact thatis the the tidal special flat caseareas of inmerger regions, event B and of Dseparation are mostlyis the the special products case of of splittingthe principal, and thenatural event drainages, of recovery whichis the specialare different case of from both themerger casesand of splittingregions. A and C.

2 Figure 19. Spatial distributions of all types of events by occurrences per 4 km2 grid: ( (aa)) appearance; appearance; ( (bb)) disappearance; disappearance; ( (cc)) contraction; (d) stability; (e) expansion; (f) merger; (g) annexation; (h) splitting; (i) separation; and (j) recovery. contraction; (d) stability; (e) expansion; (f) merger; (g) annexation; (h) splitting; (i) separation; and (j) recovery.

According to Section 3.3.1, the tidal flat cells are intensively distributed in four regions within the study area, which should potentially affect the spatial distribution of each event. This is proved with Figure 19, as all types of events are mainly distributed within or around the four regions. Accordingly, the occurrence places of these events are counted by five classes (the four intensive regions and the rest region), and the summary is drawn in Figure 20. This figure shows that 89.47% of annexation events and 75.44% of separation events are distributed within the four regions, which are the highest and lowest records among all types of events and verify the above finding. Another interesting finding is that even though the tidal flat area in region C is not the largest among the four regions, a total of eight types of events (appearance, disappearance, contraction, expansion, merger, annexation, splitting, and separation) are more in region C than elsewhere. Similarly, although the tidal flat area in region A is the smallest among the four regions, the number of event occurrences in region A is ranked as the third most among the four regions in cases of ISPRS Int. J. Geo-Inf. 2021, 10, 61 20 of 23

appearance, disappearance, and stability events and the second most among the four regions in cases of the rest event types. Therefore, we can conclude that the tidal ﬂat dynamic activities are more frequent in regions A and C than regions B and D. This may be explained by regarding the fact that the tidal ﬂat areas in regions B and D are mostly the products of the principal natural drainages, which are different from the cases of regions A and C. ISPRS Int. J. Geo-Inf. 2021, 10, x FOR PEER REVIEW 20 of 23

FigureFigure 20. 20.Percentage Percentage of of all all types types of of events events by by regions. regions.

TheThe above above spatiotemporal spatiotemporal characteristics characteristics for fo ther the events events prove prove that that the the graph graph represen- repre- tationsentation in our in caseour case study study can visualize can visualize the tracked the tracked tidal flat tidal lifecycles flat lifecycles and make and it make possible it pos- to capturesible to thecapture events the from events them. from In addition, them. In theaddition, graph operatorsthe graph in operators our case studyin our provide case study an effectiveprovide wayan effective to automatically way to automatically and exhaustively and capture exhaustively the events capture from athe large events amount from of a tidallarge flat amount lifecycles, of tidal which flat also lifecycles, highlights which that also the object highlights and lifecycle that the levels object could and providelifecycle morelevels diversified could provide dynamic more information diversified thandynamic the cellinformation level. than the cell level.

4.4. ConclusionsConclusions AnAn integratedintegrated approachapproach ofof three-levelthree-level GIS GIS framework framework and and graph graph model model is is proposed proposed inin this this study, study, which which provides provides an an effective effective way way to track, to track, represent, represent, and analyzeand analyze the dynamic the dy- activitiesnamic activities of tidal of flats tidal from flats a novel from perspective.a novel perspective. The three-level The three-level GIS framework GIS framework consists ofconsists the cell of level, the cell the level, object the level, object and level, the lifecycle and the level. lifecycle Furthermore, level. Furthermore, a variety typesa variety of eventstypes areof events defined are based defined on (1) based the numbers on (1) the of predecessorsnumbers of orpredecessors successors ofor eachsuccessors tidal flat of object;each tidal and flat (2) theobject; area and comparison (2) the area between comparison the predecessor between andthe successor.predecessor These and eventssucces- providesor. These an enhancedevents provide way to an describe enhanced the dynamicway to describe activities the of tidaldynamic flats throughoutactivities oftheir tidal lifecycles.flats throughout their lifecycles. OnOn thethe other other hand, hand, the the graph graph model model conceptualizes conceptualizes the tidal the flattidal objects flat objects as the nodes,as the and the predecessor–successor relationships as the edges. As the derivative of the graph nodes, and the predecessor–successor relationships as the edges. As the derivative of the model, a spatiotemporal database is derived simultaneously, which offers the storage and graph model, a spatiotemporal database is derived simultaneously, which offers the query features for a variety types of morphologic attributes of tidal flats. In addition, graph storage and query features for a variety types of morphologic attributes of tidal flats. In operators are introduced in this study, which assist the proposed graph model to capture addition, graph operators are introduced in this study, which assist the proposed graph the events automatically and exhaustively. model to capture the events automatically and exhaustively. In the case study, all GEE-archived Landsat imageries from 1984 to 2018 in the study In the case study, all GEE-archived Landsat imageries from 1984 to 2018 in the area are invoked, and the RF machine learning algorithm is used to extract tidal flat cells study area are invoked, and the RF machine learning algorithm is used to extract tidal from them. The tidal flat cells are assembled as tidal flat objects, which are further linked as flat cells from them. The tidal flat cells are assembled as tidal flat objects, which are fur- tidal flat lifecycles with respect to the predecessor–successor relationships. The MATLAB ther linked as tidal flat lifecycles with respect to the predecessor–successor relationships. prototype system is implemented to track, represent, and analyze the dynamic activities The MATLAB prototype system is implemented to track, represent, and analyze the dy- of tidal flats. The results imply that the geology, hydrology, and meteorology factors may affectnamic the activities dynamic of activitiestidal flats. of The tidal results flats in im theply southwest that the geology, tip of Florida hydrology, Peninsula. and meteorology factors may affect the dynamic activities of tidal flats in the southwest tip of Flori- da Peninsula. Our research is the first effort that systematically organizes the multi-level GIS framework and graph theory to track, represent, and analyze the dynamic activities of tidal flats, which also provides a novel perspective to examine the other dynamic geographic phenomena with large spatiotemporal scales. The future work will be conducted in three aspects. First, we plan to involve the whole coastal area of the conterminous US to implement this integrated approach, to not only verify its universality and robustness, but also find more diversified spatiotemporal regularities of tidal flat dynamic activities. ISPRS Int. J. Geo-Inf. 2021, 10, 61 21 of 23

Our research is the first effort that systematically organizes the multi-level GIS framework and graph theory to track, represent, and analyze the dynamic activities of tidal flats, which also provides a novel perspective to examine the other dynamic geographic phenomena with large spatiotemporal scales. The future work will be conducted in three aspects. First, we plan to involve the whole coastal area of the conterminous US to implement this integrated approach, to not only verify its universality and robustness, but also find more diversified spatiotemporal regularities of tidal flat dynamic activities. Second, we are also interested in the in-depth explorations for the natural and anthropogenic factors, which may potentially affect the spatiotemporal characteristics of tidal flat dynamic activities such as urbanization and sea level rise. Third, we noticed that the NOAA tide stations (https://tidesandcurrents.noaa.gov/) are producing high temporal resolution data of the water level along the US coast, which enables and encourages us to contribute to the interdisciplinary research collaborated with the colleagues of coastal hydrology and oceanography backgrounds.

Author Contributions: Conceptualization, Weibo Liu; Methodology, Weibo Liu and Chao Xu; Soft- ware, Chao Xu; Formal Analysis, Chao Xu and Weibo Liu; Writing- Original Draft Preparation, Chao Xu and Weibo Liu; Writing-Review & Editing, Weibo Liu. All authors have read and agreed to the published version of the manuscript. Funding: This research received no external funding. Data Availability Statement: Publicly available datasets were analyzed in this study. This data can be found here: https://developers.google.com/earth-engine/datasets/. Acknowledgments: We would like to thank the four anonymous reviewers and editors for providing valuable comments and suggestions which helped improve the manuscript greatly. Conﬂicts of Interest: The authors declare no conﬂict of interest.

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