The Spatial-Temporal Distribution of GOCI-Derived Suspended Sediment in Taiwan Coastal Water Induced by Typhoon Soudelor

remote sensing

Article The Spatial-Temporal Distribution of GOCI-Derived Suspended Sediment in Taiwan Coastal Water Induced by Typhoon Soudelor

Pham Minh Chau 1,2 , Chi-Kuei Wang 1,* and An-Te Huang 1

1 Department of Geomatics, National Cheng Kung University, No. 1 University Road, East District, Tainan City 701, Taiwan; [email protected] (P.M.C.); [email protected] (A.-T.H.) 2 Department of Civil Engineering, Vietnam Maritime University, 484 Lach Tray Street, Haiphong City 180000, Vietnam * Correspondence: [email protected]; Tel.: +886-6-275-7575 (ext. 63825)

Abstract: This paper discusses the use of a Geostationary Ocean Color Imager (GOCI) to monitor the spatial–temporal distribution of suspended sediment (SS) along the coastal waters of northern Taiwan which was affected by Typhoon Soudelor from 8 to 10 August 2015. High temporal resolution satellite images derived from GOCI were processed to generate four-day average images of SS for pre- and post-typhoon periods. By using these four-day average images, characteristics of SS along the north of Taiwan coastal water can be tracked. The results show that SS concentration increased in the four-day average image immediately after the typhoon (11–14 August), and then decreased in the four-day average image 9 to 12 days after the typhoon (19–22 August). The mouths of the Dajia River and Tamsui River were hotspots of SS, ranging from 9 to 15 g/m3 during the two post-typhoon periods. Moreover, the maximum suspended sediment (SSmax) and its corresponding time (tmax) can be computed using GOCI hourly images for the post-typhoon period from 08:30 on 11 August to 08:30 on 22 August. The results show that SSmax occurred in the west coastal water within 4 days post-typhoon, and SSmax occurred in the east coastal water 9 to 12 days post-typhoon. Furthermore, Citation: Chau, P.M.; Wang, C.-K.; an exponential decay model was used to compute the time when 90% of typhoon-induced SS was Huang, A.-T. The Spatial-Temporal dissipated after Typhoon Soudelor (t90). It was found that t90 in the mouths of the Tamsui River and Distribution of GOCI-Derived Heping River was the longest among all coastal waters of our study area, with a range of 360–480 h. Suspended Sediment in Taiwan River discharge and ocean currents with suspended sediment concentration are discussed. Coastal Water Induced by Typhoon Soudelor. Remote Sens. 2021, 13, 194. Keywords: GOCI; suspended sediment; Typhoon Soudelor; spatial–temporal distribution https://doi.org/10.3390/rs13020194

Received: 6 November 2020 Accepted: 5 January 2021 1. Introduction Published: 8 January 2021 Suspended sediment (SS) is a key part of studying shallow waters, such as coastal

Publisher’s Note: MDPI stays neu- regions, because of its influence on the marine environment and ecosystems [1]. Therefore, tral with regard to jurisdictional clai- monitoring the characteristics of SS can aid in better understanding the bio-geomorphological ms in published maps and institutio- processes and validate spatially distributed hydrodynamic and transport models in coastal nal affiliations. water regions [2]. There are many monitoring methods, such as in situ measurements with a cruise, station observations, numerical models, remote sensing, etc. In situ measurements with a cruise, numerical models, and station observations are costly and time- consuming [3,4]. Remote sensing provides a viable solution for monitoring SS in coastal Copyright: © 2021 by the authors. Li- waters because it can cover large areas at the same time. Moreover, compared with other censee MDPI, Basel, Switzerland. methods, satellite images also offer richer spatial information and can overcome opera- This article is an open access article tional cost issues due to state-of-the-art technologies. For example, the first geostationary distributed under the terms and con- ocean color observation satellite has been used for coastal water turbidity and Sentinel-3 ditions of the Creative Commons At- missions for scientific observations of the ocean [5,6]. tribution (CC BY) license (https:// Many regions around the world are affected by tropical storms, including Taiwan. creativecommons.org/licenses/by/ 4.0/). Typhoon-induced suspended sediment (SS) in the coastal water region has an impact on

Remote Sens. 2021, 13, 194. https://doi.org/10.3390/rs13020194 https://www.mdpi.com/journal/remotesensing Remote Sens. 2021, 13, 194 2 of 12

the marine environment. For instance, Typhoon Morakot had an influence on the marine environment in the East China Sea inner shelf and Okinawa Trough [7,8]. The heavy rains and episodic cyclones associated with typhoons increase the total suspended sediment, sea surface temperature, and phytoplankton. In addition, the high waves and strong wind speeds of typhoons, inducing re-suspension of bottom sediments, have been discussed [9]. The ocean surface current related to Hurricane Sandy, Typhoon Morakot, and Typhoon Saola caused the spreading of suspended sediments from the coast to the open sea [10–12]. No previous studies have used remote sensing to investigate the spatiotemporal distribution of suspended sediment in Taiwan coastal waters, induced by a typhoon. We used satellite images to do this in the north of Taiwan. Several international studies have used satellite images to assess sediment in coastal water regions. For example, remote sensing has been used to assess typhoon-induced SS concentrations. In Apalachicola Bay, Florida, USA, observations of typhoon-induced SS were conducted by using 250 m Terra MODIS (Moderate Resolution Imaging Spectro- radiometer) images during Hurricane Frances [13]. The impact of Typhoon Saomai on SS concentration in the East China Sea was calculated using Aqua and Terra MODIS images [14]. Combinations of multi-satellite images (including MODIS, MERIS (the Medium Resolution Imaging Spectrometer), and GOCI) were used to show the dynamics of suspended sediment associated with Typhoon Tembin in the East China Sea [15]. The sediment transport in the Taiwan Strait induced by Typhoons Soulik and Morakot has been moni- tored by using Aqua MODIS images [16,17]. The spatial–temporal distribution of SS has not yet been considered because of the limitation of quality data under typhoons. Therefore, this paper tries to bridge the gap between the spatial–temporal distribution of SS induced by a typhoon and data limitations. The Geostationary Ocean Color Imager (GOCI), a satellite sensor, can overcome the limitation of quality data under typhoon weather conditions due to its temporal resolution [18]. The GOCI is operated by the Korea Ocean Satellite Center (KOSC) at the Korea Institute of Ocean Science and Technology (KIOST). It is the first ocean color satellite placed in geostationary orbit to provide eight hourly images during the daytime (from 08:30 to 15:30 local time at one-hour intervals) with a spatial resolution of 500 m. GOCI covers about 2500 km × 2500 km centering on the Korean Peninsula (at the center of 130◦ E, 36◦ N), including the north of Taiwan. It has six visible bands from 412 to 680 nm and two near- infrared bands at 745 and 865 nm. The bands at wavelengths of 555 and 660 nm are used for suspended sediment extraction [19–22]. All of the existing studies related to GOCI-derived suspended sediment focused on monitoring the temporal variation of water turbidity and the diurnal dynamics of suspended sediment in coastal water. For instance, GOCI hourly images have been used to monitor the diurnal and seasonal variability of suspended sediment concentration in a macro-tidal estuary [23]. GOCI images have been used to monitor the suspended sediment in Taihu Lake [24] and the coastal waters of Zhejiang, China [25], as well as Gyeonggi Bay on the west coast of Korea [26]. Moreover, GOCI also monitors long-term suspended sediment concentration and estimates ocean surface currents hourly [27]. However, using GOCI to monitor the spatiotemporal distribution of typhoon-induced SS in coastal waters has not been considered in previous case studies. This study used GOCI to monitor the spatial and temporal distribution of SS pre- and post-Typhoon Soudelor, which made landfall in Taiwan in August 2015. Furthermore, by taking advantage of GOCI with time-series hourly images of SS after the typhoon, the temporal decay of the SS pattern can be computed by an exponential regression, and the time SS recovered to its pre-typhoon value can be estimated. This approach, which is the core of the study, quantifies the typhoon-induced spatial and temporal distribution of SS along Taiwan coastal water. Finally, factors such as river discharge and ocean currents could have affected the discussed spatiotemporal distribution of SS. Remote Sens. 2021, 13, x FOR PEER REVIEW 3 of 12

2. Materials and Methods 2.1. Study Area Remote Sens. 2021, 13, 194 3 of 12 The study area is located on the northern coast of Taiwan (Figure 1). There are 9 rivers administered by the Taiwan central government (ATCG) [28]. Seven of the rivers are on the west side of Taiwan Island, and two are on the east side. 2. MaterialsThe statistics and Methods of the annual discharge of the 9 rivers were provided by the Taiwan 2.1.River Study Restoration Area Network [28]. Tamsui River has the largest annual discharge of 7443 m3/s,The followed study area by Lanyang is located and on theDajia northern with 2773 coast m of3/s Taiwan and 2569 (Figure m3/s,1). respectively. There are 9 rivers Other administeredrivers have a by lower the Taiwan discharge central of less government than 2000 (ATCG) m3/s, and [28 ]. the Seven Fengshan of the riversRiver hasare on the thelowest west discharge side of Taiwan of 376 Island, m3/s. and two are on the east side.

FigureFigure 1. 1.( a(a)) Visualization Visualization of of Geostationary Geostationary Ocean Ocean Color Color Imager Imager (GOCI) (GOCI) coverage coverage area; area; ( b(b) study) study area. area. The statistics of the annual discharge of the 9 rivers were provided by the Taiwan River 2.2. Typhoon Soudelor Restoration Network [28]. Tamsui River has the largest annual discharge of 7443 m3/s, followedTyphoon by Lanyang Soudelor and formed Dajia with in the 2773 middle m3/s andof the 2569 Pacific m3/s, Ocean respectively. on 20 July Other 2015, rivers and havebecame a lower a super discharge typhoon of (category less than 5 2000 on the m3 /s,Saffir and–Simpson the Fengshan hurricane River wind has scale) the lowest on 29 dischargeJuly [29]. ofTyphoon 376 m3/s. Soudelor made landfall in the east of Taiwan at 04:40 local time on 8 August 2015 and brought torrential rain. The typhoon then moved north-westwards 2.2.through Typhoon eastern Soudelor China and degraded to a tropical depression on 9 August 2015 [30–33]. Typhoon Soudelor formed in the middle of the Paciﬁc Ocean on 20 July 2015, and became2.3. GOCI a superSatellite typhoon Images (category 5 on the Safﬁr–Simpson hurricane wind scale) on 29 JulyIn [ 29this]. Typhoonstudy, GOCI Soudelor level- made2 images landfall were indownloaded the east of Taiwanfrom the at NASA 04:40 local Ocean time Color on 8website August ( 2015https://oceancolor.gsfc.nasa.gov/ and brought torrential rain.). The Then, typhoon SS was then extracted moved from north-westwards GOCI hourly throughimages easternwith a spatial China andresolution degraded of 500 to a m. tropical These depression hourly images on 9 Augustwere binned 2015 [ 30using–33]. the arithmetic mean algorithm, implemented in SeaDAS [34,35], to create daily SS images 2.3.with GOCI a spatial Satellite resol Imagesution of 500 m (Figure 2). InDuring this study, the period GOCI of level-2 Typhon images Soudelor, were downloadedcloud cover caused from the data NASA voids Ocean in many Color of websitethe hourly (https://oceancolor.gsfc.nasa.gov/ GOCI images. The problem was ).more Then, severe SS was on extracted8–10 and 15 from–18 GOCIAugust, hourly when imagesGOCI images with a spatial were not resolution available of for 500 our m. study These area hourly (Figure images 2). were To b binnedetter visualize using the the arithmeticspatial distribution mean algorithm, of typhoon implemented-induced in SS, SeaDAS i.e., minimize [34,35], tothe create data dailyvoids, SS 4 images daily GOCI with aimages spatial were resolution further of binned 500 m (Figure to generate2). a 4-day average image with a spatial resolution of 500During m. the period of Typhon Soudelor, cloud cover caused data voids in many of the hourlyA GOCI 4-day images. average The image problem immediately was more before severe the on typho 8–10on and (푆푆̅̅̅ 15–184−7), binned August, from when the GOCIdaily images SS images were of not 4– available7 August for 2015, our study was generated. area (Figure Similarly,2). To better a 4 visualize-day average the spatial image distribution of typhoon-induced SS, i.e., minimize the data voids, 4 daily GOCI images immediately after the typhoon (푆푆̅̅̅11−14), binned from 11–14 August, was generated. wereMoreover, further to binned monitor to generatethe decrease a 4-day in suspended average image sediment, with a 4 spatial-day average resolution image of 500 9 to m. 12 A 4-day average image immediately before the typhoon (SS4−7), binned from the daily SS images of 4–7 August 2015, was generated. Similarly, a 4-day average image immediately after the typhoon (SS11−14), binned from 11–14 August, was generated. Moreover, to monitor the decrease in suspended sediment, a 4-day average image 9 to 12 days after the typhoon (SS19−22), binned from the daily SS images of 19–22 August, was generated. Figure2 shows the data processing of GOCI images in this study. Remote Sens. 2021, 13, x FOR PEER REVIEW 4 of 12

Remote Sens. 2021, 13, 194 4 of 12 days after the typhoon (푆푆̅̅̅19−22), binned from the daily SS images of 19–22 August, was generated. Figure 2 shows the data processing of GOCI images in this study.

̅̅̅ ̅̅̅ ̅̅̅ Figure 2. GOCI data processing for generating SS푆푆44−−77,, SS푆푆1111−−14,, andand SS푆푆1919−−22.. SS: suspended sediment.sediment.

2.4. Quantitative R Retrievaletrieval A Algorithmlgorithm of SS In thisthis paper,paper, SS SS images images were were derived derived from from level-2 level GOCI-2 GOCI images images by using by using the algorithm the algo- developedrithm developed by Moon by Moon et al. (2010) et al. (2010) based based on in-situ on in SS-situ samples SS samples (Equation (Equation (1)) [20 (1))]: [20]:

SS푆푆==945.07945.07××(Rrs(푅푟푠(555(555)))1.137)1.137 (1)(1) where Rrs (555) is remote sensing reflectance at a wavelength of 555 nm, and SS is re- where Rrs (555) is remote sensing reﬂectance at a wavelength of 555 nm, and SS is reported ported in g/m3. The algorithm was implemented in the GOCI Data Processing System in g/m3. The algorithm was implemented in the GOCI Data Processing System (GDPS) by (GDPS) by KOSC [36]. KOSC [36].

2.5. Temporal DecayDecay ofof SSSS afterafter TyphoonTyphoon When showingshowing the temporal history of hourly SS of a GOCI pixel (Figure3 3),), the the maximum SS value (SS푆푆푚푎푥max) was reached in a few days, varying with pixel locations, after Typhoon Soudelor made landfall on Taiwan. In In this this research, research, 푆푆SS푚푎푥max ofof eacheach pixelpixel waswas determined as as the the largest largest hourly hourly SS SS value value of that of that pixel pixel from from 11–22 11 August.–22 August. The time The when time thewhe pixeln the reachespixel reachesSSmax is푆푆 denoted푚푎푥 is denoted as tmax. as 푡푚푎푥. An interesting feature in the temporal history of SS (Figure3) is its decaying pattern, where the SS value decreases after tmax. We proposed using a decaying model to quantify the pattern by ﬁtting an exponential curve to hourly SS data for each GOCI pixel via regression. It was found (with internal trials) that a robust result of the regression was obtained for GOCI pixels that had more than 5 hourly SS values available after their tmax. Figure3 shows an example of the regression result depicting the decaying exponential model after tmax, despite the GOCI pixel not having data from 15 to 18 August due to cloud cover.

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FigureFigure 3. 3. ExampleExample of of temporal temporal decay decay of of suspended suspended sediment sediment ( (SSSS).). Corresponding Corresponding 푆푆SS9090 with t푡9090are are denoted. denoted.

Ideally,An interesting taking advantagefeature in the of this temporal decaying history model, of theSS (Figure time for 3) the is pixelits decayin to returng pat- to itstern, pre-typhoon where the SS state value could decreases be estimated after 푡 using푚푎푥. We the proposed regression using result. a decaying However, model it was to foundquantify that the the pattern decaying by exponential fitting an exponential model of many curve GOCI to hourly pixels only SS data approaches for each the GOCI pre- typhoonpixel via stateregression. asymptotically, It was found i.e., the(with regression internal linetrials) shown that ina robust Figure 3re doessult of not the intersect regres- withsion wasSS4− obtained7. Instead, for we GOCI demonstrate pixels that the had use more of this than model 5 hourly by computing SS values available dissipation after of 90% increased SS. This would ﬁt for the application of coastal ecology management, as the their 푡푚푎푥. Figure 3 shows an example of the regression result depicting the decaying ecosystem is resilient to a certain increase in SS for a short period of time. exponential model after 푡푚푎푥, despite the GOCI pixel not having data from 15 to 18 AugustThe due amount to cloud of increased cover. SS, denoted as SSa, for each GOCI pixel, can be computed as Ideally, taking advantage of this decaying model, the time for the pixel to return to SS = SS − SS (2) its pre-typhoon state could be estimateda usingmax the4 − regression7 result. However, it was found that the decaying exponential model of many GOCI pixels only approaches the and SS90, which represents 90% of SSa, is dissipated from its maximum value (Figure3). pre-typhoon state asymptotically, i.e., the regression line shown in Figure 3 does not in- ̅̅̅ tersect with 푆푆4−7. Instead, we demonstrateSS90 = SSmax the− 0.9 use× ofSS thisa model by computing dissipa-(3) tion of 90% increased SS. This would fit for the application of coastal ecology management,Furthermore, as the ecosystem the timeis resilient corresponding to a certain to increaseSS90 can in beSS for identiﬁed a short viaperiod the of regressed time. exponentialThe amount decay of and increased is denoted SS, denoted as t90 (Figure as 푆푆3).푎, for each GOCI pixel, can be computed as 3. Results ̅̅̅ 3.1. Spatial–Temporal Analysis of SS Pre-푆푆푎 and= 푆푆 Post-Typhoon푚푎푥 − 푆푆4−7 Soudelor (2)

and During푆푆90, which the pre-typhoonrepresents 90% period of 푆푆 (SS푎, is4− 7dissipatedin Figure from4a),SS its withmaximum a concentration value (Figure of 3–63). g/m3 was mainly distributed along the west coastal water. The mouths of both the Dajia River and Tamsui River had an SS greater than 6 g/m3. The SS concentration in the east coastal water was less than 3 g/m푆푆90 3=. 푆푆푚푎푥 − 0.9 × 푆푆푎 (3)

DuringFurthermore, the post-typhoon the time corresponding period of 11–14 to August 푆푆90 can (SS be11− identified14 in Figure via4b), the the regressed SS of the Taiwanexponential coastal decay water and generally is denoted increased. as 푡90 (Figure The SS 3). along the west and east coastal waters increased to values greater than 6 and 3 g/m3, respectively. Meanwhile, the mouths of the 3 Dajia3. Results River and Tamsui River (west coastal water) had SS values greater than 9 g/m and the Lanyang River mouth (east coastal water) had an SS value greater than 6 g/m3. 3.1. Spatial–Temporal Analysis of SS Pre- and Post-Typhoon Soudelor During the post-typhoon period of 19–22 August (SS in Figure4c), the general ̅̅̅ 19−22 distributionDuring ofthe SS pre along-typhoon the west period coastal (푆푆 water4−7 in was Figure similar 4a), to SS that with of thea concentration pre-typhoon periodof 3–6 3 (Figureg/m was5a), mainly except distributed for the mouths along of the west Dajia coastal River and water. Tamsui The Rivermouths having of both an SSthe value Dajia 3 greaterRiver and than Tamsui 9 g/m 3River. A belt had with an relativelySS greater high than SS 6 valuesg/m . The (greater SS concentration than 3 g/m3) wasin the found east alongcoastal the water east was coastal less water than 3 with g/m a3. width of approximately 3 km. In addition, the mouths ̅̅̅ of bothDuring the Lanyang the post- Rivertyphoon and period Heping of River 11–14 had August SS values (푆푆11− greater14 in Figure than 64b), g/m the3. SS of the Taiwan coastal water generally increased. The SS along the west and east coastal waters increased to values greater than 6 and 3 g/m3, respectively. Meanwhile, the mouths of the

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3 Dajia River and Tamsui River (west coastal water) had SS values greater than 9 g/m3 and 3 the Lanyang River mouth (east coastal water) had an SS value greater than 6 g/m3. During the post-typhoon period of 19–22 August (SS̅̅̅19−22 in Figure 4c), the general During the post-typhoon period of 19–22 August (SS̅̅̅19−22 in Figure 4c), the general distribution of SS along the west coastal water was similar to that of the pre-typhoon period (Figure 5a), except for the mouths of the Dajia River and Tamsui River having an SS 3 3 value greater than 9 g/m3. A belt with relatively high SS values (greater than 3 g/m3) was Remote Sens. 2021, 13, 194 value greater than 9 g/m . A belt with relatively high SS values (greater than 3 g/m )6 was of 12 found along the east coastal water with a width of approximately 3 km. In addition, the 3 mouths of both the Lanyang River and Heping River had SS values greater than 6 g/m3.

Figure 4. Spatial distribution of SS pre- and post-typhoon: (a) 푆푆̅̅̅4−7; (b) 푆푆̅̅̅11−14; (c) 푆푆̅̅̅19−22. Figure 4. Spatial distribution of SS pre-pre- and post-typhoon:post-typhoon: (a)) SS푆푆̅̅̅44−−7;(; (b)) SS푆푆̅̅̅11−1414;;( (c) SS푆푆̅̅̅19−22.

Figure 5. Temporal difference ofof SS:SS: ((aa)) SS푆푆̅̅̅1111−−1414−−SS푆푆̅̅̅4−7;;( (b) SS푆푆̅̅̅1919−−22 − SS푆푆̅̅̅1111−1414 .. Figure 5. Temporal difference of SS: (a) 푆푆̅̅̅11−14 − 푆푆̅̅̅4−7; (b) 푆푆̅̅̅19−22 − 푆푆̅̅̅11−14 .

Comparing the SS between the post-typhoon period of 11–14 August (SS11−14) and pre- Comparing the SS between the post-typhoon period of 11–14 August (푆푆̅̅̅11−14) and Comparing the SS between the post-typhoon period of 11–14 August3 (푆푆̅̅̅11−14) and typhoon (SS4−7), it was found that all pixels increased more than 1 g/m (Figure3 5a), except pre-typhoon (푆푆̅̅̅4−7), it was found that all pixels increased more than 1 g/m3 (Figure 5a), pre-typhoon (푆푆̅̅̅4−7), it was found that all pixels increased more than 1 g/m (Figure 5a), exceptfor a tongue-shaped for a tongue-shaped area (denoted area (denoted by arrows by inarrows Figure in5 a)Figure located 5a) 10–20 located km 10 off–20 the km west off exceptcoastal for water a tongue of Taiwan.-shaped In addition,area (denoted the distribution by arrows in of increasedFigure 5a) SS located showed 10– prominent20 km off the west coastal water of Taiwan. In addition, the distribution of increased SS showed theheterogeneity west coastal along water the of coastal Taiwan. water In addition, of Taiwan. the A distribution high increase of in increased SS (i.e., greater SS sho thanwed prominent3 heterogeneity along the coastal water of Taiwan. A high increase in SS (i.e., 6.0 g/m ) was found3 at the mouths of the Dajia, Daan, Tamsui, and Lanyang Rivers. Three greater than 6.0 g/m3) was found at the mouths of the Dajia, Daan, Tamsui, and Lanyang greaterregions than with 6.0 a lowg/m SS) was increase found (less at the than mouths 2 g/m of3 )the along Dajia, the Daan, coast, Tamsui, from the and Daan Lanyang River mouth to Zhonggan River mouth, the Fengshan River mouth to Tamsui River mouth, and the Tamsui River to Lanyang River mouth, were also identiﬁed (Figure5a). Comparing the SS between two post-typhoon periods of 19–12 August (SS19−22) and 11–14 August (SS11−14), it was found that most of the pixels decreased while some remained with increased SS values (Figure5b). Most of the pixels decreased to less than 3 g/m 3. The Lanyang River mouth showed the most signiﬁcant decrease of 6 g/m3, followed by the Zhonggang and Tauqian River mouths, with a reduction of 3 g/m3. In contrast, the Dajia River mouth showed an increased SS value greater than 3 g/m3 and the Heping Remote Sens. 2021, 13, x FOR PEER REVIEW 7 of 12

Rivers. Three regions with a low SS increase (less than 2 g/m3) along the coast, from the Daan River mouth to Zhonggan River mouth, the Fengshan River mouth to Tamsui River mouth, and the Tamsui River to Lanyang River mouth, were also identified (Figure 5a). Comparing the SS between two post-typhoon periods of 19–12 August (푆푆̅̅̅19−22 ) and 11–14 August (푆푆̅̅̅11−14), it was found that most of the pixels decreased while some Remote Sens. 2021, 13, 194 remained with increased SS values (Figure 5b). Most of the pixels decreased to less than7 of 12 3 g/m3. The Lanyang River mouth showed the most significant decrease of 6 g/m3, followed by the Zhonggang and Tauqian River mouths, with a reduction of 3 g/m3. In contrast, the Dajia River mouth showed an increased SS value greater than 3 g/m3 and the Heping 3 River mouth showed an increasedincreased SSSS valuevalue ofof 66 g/mg/m3.. It It i iss also interesting to note thatthat neighboring regions with increased SS (greater than 3 g/m3) were found northeast of the neighboring regions with increased SS (greater than 3 g/m3) were found northeast of the Tamsui River mouth and north of the Lanyang River mouth. The regions with increased SS Tamsui River mouth and north of the Lanyang River mouth. The regions with increased are indicated by arrows in Figure5b. SS are indicated by arrows in Figure 5b. With the GOCI average SS data, it was observed that SS in the west coastal water With the GOCI average SS data, it was observed that SS in the west coastal water was consistently greater than that on the east coastal water regardless of the effect of was consistently greater than that on the east coastal water regardless of the effect of Typhoon Soudelor. In addition, hotspots of high SS value (greater than 9 g/m3) were Typhoon Soudelor. In addition, hotspots of high SS value (greater than 9 g/m3) were found at the Dajia and Tamsui River mouth in the two post-typhoon periods (SS11−14 and found at the Dajia and Tamsui River mouth in the two post-typhoon periods (푆푆̅̅̅11−14 SS − ). With the GOCI data for pre-typhoon (SS − ) and post-typhoon (SS − ), it was and19 푆푆̅̅22̅ ). With the GOCI data for pre-typhoon4 (7푆푆̅̅̅ ) and post-typhoon11 (푆푆̅14̅̅ ), it observed19− that22 the Taiwan coastal water showed a prominent4−7 increase in SS induced11−14 by was observed that the Taiwan coastal water showed a prominent increase in SS induced Typhoon Soudelor. by Typhoon Soudelor.

3.2. SS푆푆푚푎푥max andand tmax푡푚푎푥

FigureFigure 66aa showsshows aa visualizationvisualization ofof thethemaximum maximum suspendedsuspended sedimentsediment ((SS푆푆푚푎푥max ) forfor each GOCI pixel of Taiwan coastalcoastal waterwater during the post-typhoonpost-typhoon period (from 08:3008:30 onon 11 August to 15:30 on 22 August). Figure Figure 66bb shows its corresponding time ((푡tmax푚푎푥)) derivedderived from GOCI hourly data.

Figure 6. (a) Maximum suspended sediment (SS ); (b) its corresponding time (t ). Figure 6. (a) Maximum suspended sediment (푆푆푚푎푥max); (b) its corresponding time (푡max푚푎푥). Generally, the west coastal water from the Dajia River mouth to the Tamsui River Generally, the west coastal water from the Dajia3 River mouth to the Tamsui River mouth showed SSmax in the range of 9–15 g/m3 with a corresponding tmax of 0–80 h mouth showed 푆푆푚푎푥 in the range of 9–15 g/m with a corresponding 푡푚푎푥 of 0–80 h (Figure6b). This indicates that most of the west coastal water reached SSmax during the (Figure 6b). This indicates that most of the west coastal water reached 푆푆푚푎푥 during the post-typhoon period of 11–14 August (within four days after Typhoon Soudelor). The post-typhoon period of 11–14 August (within four days after Typhoon Soudelor). The exceptions were two strips with t greater than 264 h located 5 and 15 km away from exceptions were two strips with 푡max greater than 264 h located 5 and 15 km away from the Dajia River mouth (denoted푚푎푥 by blue arrows in Figure6b) , and three regions with the Dajia River mouth (denoted by blue arrows in Figure 6b), and three regions with 푡 t greater than 216 h located near the Tamsui River mouth (denoted by red arrows푚푎푥 in greatermax than 216 h located near the Tamsui River mouth (denoted by red arrows in Figure Figure6b) . Further small regions with t greater than 264 h sporadically occurred in the 6b). Further small regions with 푡 maxgreater than 264 h sporadically occurred in the coastal water from the Houlong River푚푎푥 mouth to Zhonggang River mouth and the Feshang River mouth to Tamsui River mouth. It is interesting to note that the general appearance of the SSmax of the northeast coastal water (indicated by the dotted white rectangle) was lower than the west and east coastal 3 waters, with a range of less than 6 g/m , while its tmax value showed a large variation of 0–272 h. Most of the coastal water of the northern coast had SSmax values in the range of 3 3–6 g/m with tmax of 24–80 h (within four days after Typhoon Soudelor). At the west and east ends (indicated by black and white arrows, respectively, in Figure6) of the northeast 3 coast, SSmax was in the range of 6–9 g/m with a corresponding tmax greater than 264 h. It also indicates that both SSmax and tmax of the northern coastal waters are continuous data Remote Sens. 2021, 13, x FOR PEER REVIEW 8 of 12

coastal water from the Houlong River mouth to Zhonggang River mouth and the Feshang River mouth to Tamsui River mouth. It is interesting to note that the general appearance of the 푆푆푚푎푥 of the northeast coastal water (indicated by the dotted white rectangle) was lower than the west and east 3 coastal waters, with a range of less than 6 g/m , while its 푡푚푎푥 value showed a large variation of 0–272 h. Most of the coastal water of the northern coast had 푆푆푚푎푥 values in 3 the range of 3–6 g/m with 푡푚푎푥 of 24–80 h (within four days after Typhoon Soudelor). At Remote Sens. 2021, 13, 194 the west and east ends (indicated by black and white arrows, respectively, in Figure8 6) of of 12 3 the northeast coast, 푆푆푚푎푥 was in the range of 6–9 g/m with a corresponding 푡푚푎푥 greater than 264 h. It also indicates that both 푆푆푚푎푥 and 푡푚푎푥 of the northern coastal waters are continuous data from the west and east coastal water. Even though there are from the west and east coastal water. Even though there are no ATCG rivers in this region, no ATCG rivers in this region, 푆푆푚푎푥 and 푡푚푎푥 have been linked to river-derived sus- SS t pendedmax and sediment.max have been linked to river-derived suspended sediment. The east coastal water generally took a long time to reach SSmax, with a range of The east coastal water generally took a long time to reach 푆푆푚푎푥, with a range of 9– 9–15 g/m3 compared to the west and northeast coastal waters, with t of 192–272 h 15 g/m3 compared to the west and northeast coastal waters, with 푡 ofmax 192–272 h (9–12 (9–12 days after Typhoon Soudelor). The exception is the coastal water푚푎푥 near the Lanyang days after Typhoon Soudelor). The exception is the coastal water near the Lanyang River River mouth, which reached an SS with t of 24–80 h (within 4 days after the typhoon). mouth, which reached an 푆푆 withmax 푡 max of 24–80 h (within 4 days after the typhoon). The coastal water near Heping푚푎푥 River mouth푚푎푥 showed SS with a range of 6–15 g/m3 The coastal water near Heping River mouth showed 푆푆max with a range of 6–15 g/m3 corresponding to t greater than 240 h (11–12 days after푚푎푥 the typhoon). Interestingly, corresponding to 푡max greater than 240 h (11–12 days after the typhoon). Interestingly, a a region north of the푚푎푥 Lanyang River mouth (indicated by yellow arrows) also showed region north of the Lanyang River3 mouth (indicated by yellow arrows) also showed a a local high SSmax of 9–15 g/m with a corresponding tmax value similar to the Heping local high 푆푆 of 9–15 g/m3 with a corresponding 푡 value similar to the Heping River mouth.푚푎푥 푚푎푥 River mouth.

3.3. SS푆푆9090 and t90푡90

Figure7 7aa shows shows a a visualization visualization of ofSS 푆푆9090, which, which means means 90% 90% of increasing of increasing SS dissipated SS dissi- patedfrom its from maximum its maximum value for value each for GOCI each pixel GOCI of Taiwanpixel of coastal Taiwan water. coastal Figure water.7bshows Figure the 7b showscorresponding the corresponding time (t90) via time regressed (푡90) via exponential regressed exponential decay. decay.

Figure 7. (a) SS concentration of each pixel reduced to 90% after typhoon-induced impact; (b) its corresponding time t90. Figure 7. (a) SS concentration of each pixel reduced to 90% after typhoon-induced impact; (b) its corresponding time 푡90. SS SS White contour lines indicate 푆푆̅̅̅44−−77;; redred contourcontour lineslines indicateindicate 푆푆9090..

The comparison between SS90 (Figure7a) and SS4−7 (Figure4a) indicates that SS90 The comparison between 푆푆90 (Figure 7a) and 푆푆̅̅̅4−7 (Figure 4a) indicates that 푆푆90 was similar to the pre-typhoon state (SS4−7), except for the SS of the Tamsui River mouth was similar to the pre-typhoon state (푆푆̅̅̅4−7), except for the SS of the Tamsui River mouth (within 3–6 g/m3; indicated by arrows in Figure7a). Particularly, SS within 3–6 g/m 3 was (within 3–6 g/m3; indicated by arrows in Figure 7a). Particularly, SS within 3–6 g/m3 was observed mainly in the west coastal water, while it was less than 3 g/m3 in the east coastal observed mainly in the west coastal water, while it was less than 3 g/m3 in the east coastal waters; SS greater than 6 g/m3 also only appeared at the Tamsui and Dajia River mouths waters; SS greater than 6 g/m3 also only appeared at the Tamsui and Dajia River mouths (Figures7a and4a). There was a slight difference in SS located 40 km off the Tamsui River (Figure 7a and Figure 4a). There was a slight3 difference in SS located 40 km off the Tam- mouth. SS90 showed that the SS of 3 g/m extended farther into the sea than in SS4−7. The west coastal waters from the Dajia River mouth to the Tamsui River mouth showed a t90 of more than 240 h, except for a tongue-shaped area with a range of 10–40 km off the west coastal water (indicated by arrows in Figure7b), showing a t90 of less than 240 h. The Tamsui River mouth extending within 20 km of the coastline indicated a t 90 with a range of 240–480 h. The northeast coastal water to the Lanyang River mouth (Figure7a) showed an SS90 associated with a t90 of less than 240 h, except for small scattered regions that appeared closest to the coastline with a t90 of around 480 h (Figure7b). Lastly, the east coastal waters from the Lanyang to Heping River mouth showed a t90 of 360–480 h (Figure7b). It should be noted that some pixels available in the SS90 are not visualized by the t90. There are two cases in which the decaying model works without advantages, leading Remote Sens. 2021, 13, 194 9 of 12

to the pixel in t90 not being visualized. When GOCI pixels (indicated by a red rectangle in Figure7 ) have a gap between SSmax and SS4−7, which is close (Figure3), t90 is not visualized due to no decay. When the number of GOCI pixels after tmax has fewer than ﬁve values, it is also not visualized because of the lack of data (indicated by the black rectangle in Figure7). Otherwise, the proposed methodology, which uses an exponential temporal decaying model, shows a distinct advantage when GOCI pixels have more than ﬁve hourly SS values available after their tmax. It is possible to compute the t90 with robust results post-typhoon.

4. Discussion According to previous studies in Taiwan, one possible reason for the SS derived from a typhoon is that it is strongly affected by river discharge. For example, using a case study in Choshui River, Taiwan, and Typhoon Mindulle, the authors of [37] indicated that in a floodplain, more than half of the suspended sediment originating from mountain rivers running into Taiwan coastal waters was generated by river discharge. The authors of [38] observed the Jhoushui River and an adjacent coastal zone in the Taiwan Strait and summarized that the river discharges most of the sediment during the relatively short periods of torrential rain often associated with typhoons. Moreover, the authors of [39,40] indicated that suspended sediment discharge during typhoon events was linked to landslides and rainfall in Taiwan. The authors of [41] considered the impact of typhoons on sediment discharge in Taiwan. River discharge also impacted the change in sediment concentration in the Tamsui River, as discussed in [42]. Even though we have the same opinion, there are no recorded data to support the Typhoon Soudelor case. Therefore, examining the mechanical factors, such as river discharge, related to typhoons is beyond the scope of this study. We only discuss this based on the mean annual discharge data, which were provided by the Taiwan River Restoration Network [28,43]. In terms of the mean annual discharge related to the nine central rivers administered by ATCG, the Tamsui River supplies the largest amount with 7443 m3/s, followed by the Lanyang and Dajia Rivers with 2773 and 2596 m3/s, respectively. This is the reason why the Tamsui, Dajia, and Lanyang River mouths act as hotspots with high SS values (SSmax 3 above 9 g/m ) during the two post-typhoon periods (SS11−14 and SS19−22). Meanwhile, other rivers show a mean annual discharge lower than 2000 m3/s, and they influence the 3 coastal regions with SSmax values in the range of 6–9 g/m . The central rivers are mainly located in the western part of Taiwan, which may be why many GOCI pixels of the west coastal waters are more influenced than those of the east coastal waters, regardless of the effect of Typhoon Soudelor. Other factors such as tide level, waves, wind speed, and surface currents that impact the spatiotemporal distribution of SS should also be discussed. All of these factors have been considered by many scientists. According to the authors of [44], by using shipboard observations for estimation, transport and tidal currents in the Taiwan Strait were northward (into the East China Sea). This is similar to the ocean surface current from GOCI satellite imagery in summer around the north of Taiwan coastal water being northward [45]. Therefore, we also believe that after Typhoon Soudelor, the ocean surface was a major factor in increasing the outbreak of suspended sediment north of Taiwan. However, due to the limitation of recorded data on Typhoon Soudelor, the transport mechanism responsible for the sediment warrants further investigation.

5. Conclusions This paper proposes a new approach using the GOCI to monitor the spatial and temporal distribution of suspended sediment in coastal areas affected by typhoons. Remote sensing technology was used instead of other methods such as in situ measurements, numerical models, and station observations to track post-typhoon sediment concentration in Taiwan coastal waters. Remote Sens. 2021, 13, 194 10 of 12

The spatial distribution of SS has been highlighted by using the GOCI four-day average image of SS pre- and post-Typhoon Soudelor. As a result, several pixels with an SS above 6 g/m3 in the west coastal waters were consistently more signiﬁcant than in the east coastal waters regardless of the typhoon. The Dajia and Tamsui River mouths 3 were hotspots of increased SS and SSmax (above 9 g/m ) during two post-typhoon periods (11–14 and 29–22 August). According to the GOCI hourly data after the typhoon, SSmax was in the range of 3 6–15 g/m , corresponding to tmax within four days in the west coastal water, while the east coastal water was 9–12 days. Furthermore, using exponential regression decay to visualize SS90 for each GOCI pixel in Taiwan coastal water indicates that SS90 was in an asymptotic pre-typhoon state. The corresponding time t90 shows that GOCI pixels in both the Tamsui and Heping River mouths generally took the longest time, in a range of 360–480 h. River discharge could have a signiﬁcant impact on the post-typhoon sediment characteristics of Taiwan coastal waters. Other factors such as tide level, waves, wind speed, and surface currents could also affect the spatiotemporal distribution of suspended sediment. We suggest that this should be investigated in the future by using a successfully recorded dataset with a new typhoon.

Author Contributions: Conceptualization, C.-K.W.; Data curation, P.M.C.; Formal analysis, P.M.C.; Investigation, P.M.C.; Methodology, P.M.C., C.-K.W. and A.-T.H.; Project administration, C.-K.W.; Soft- ware, A.-T.H.; Supervision, C.-K.W.; Validation, P.M.C.; Visualization, P.M.C. and A.-T.H.; Writing— original draft, P.M.C. and A.-T.H.; Writing—review & editing, C.-K.W. All authors have read and agreed to the published version of the manuscript. Funding: The APC was supported in part by Higher Education Sprout Project, Ministry of Education to the Headquarters of University Advancement at National Cheng Kung University (NCKU). Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable. Data Availability Statement: The data that support the findings of this study are available from the corresponding author upon reasonable request. Acknowledgments: The authors thank NASA for providing free GOCI satellite images and the SeaDAS application. We also thank the Korea Ocean Satellite Center for providing the GDPS application. The authors would also like to thank the Taiwan River Restoration Network for sharing river system information. Finally, the authors deeply acknowledge anonymous reviewers for their constructive comments and suggestion toward improving this manuscript. Conflicts of Interest: The authors declare no conflict of interest.

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