Remote sensing of chlorophyll-a as a measure of red tide in Tokyo Bay using hotspot analysis

Remote Sensing Applications: Society and Environment 2 (2015) 11–25

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Remote Sensing Applications: Society and Environment journal homepage: www.elsevier.com/locate/rsase

Remote sensing of chlorophyll-a as a measure of red tide in Tokyo Bay using hotspot analysis Ali P. Yunus a,n, Jie Dou a, N. Sravanthi b a b

Department of Natural Environmental Studies, The University of Tokyo, Kashiwanoha 5-1-5, 277 8563 Japan Centre for Earth and Space Sciences, University of Hyderabad, Hyderabad 500046, India

a r t i c l e i n f o

abstract

Article history: Received 10 June 2015 Received in revised form 20 September 2015 Accepted 29 September 2015 Available online 22 October 2015

Tokyo Bay plays significant roles in Japan's economic and social development; however, the bay is facing the threat on water quality degradation due to harmful algal productions. Water quality parameters are measured at few stations in Tokyo Bay by respective agencies but is limited in time and space. This paper presents empirical models for continual Chl-a retrieval and red tide detection in Tokyo Bay water using satellite reflectance data derived from Landsat OLI sensor. The models use regression results from 38 samples, which obtained for a period between January and December of 2014. Based on the model fit, band ratios of blue and green were used to retrieve the Chl-a (R2 ¼0.63, pvalue o 0.05). Seasonal pattern of Chl-a were then studied using the images obtained for the study period. Results shows that Chl-a concentrations during summer months are associated with high phytoplankton activity, and that for winter months are accompanying with low phytoplankton activity in Tokyo Bay. A simple hotspot model based on Getis-Ord Gi* were then proposed to detect the red tide events. Based on the hotspot analysis, z score for the dates of Landsat images were calculated and mapped. The high z scores obtained from Chl-a maps often corresponds with the measured red tide events. Results confirm the potential of spatial autocorrelation techniques for the detection of red tide breakouts from Chl-a retrieved Landsat 8 OLI. & 2015 Elsevier B.V. All rights reserved.

Keywords: Tokyo Bay Red tides Chlorophyll retrieval Ocean color

1. Introduction Harmful algal blooms (HABs) or commonly referred term “red tides” are one of several serious and increasing ecological and economical problem in coastal waters. The geographic occurrences of red tides is widespread, and the harmful algal populations have been documented in sea waters around the world (Anderson, 1994; Walsh and Steidinger, 2001; Okaichi, 2004; Du Yoo et al., 2013). The spatial extent of their presence is highly variable ranging localized, occurring in bays or estuaries to massive, and covering several hundreds of square kilometers. Similarly, n

Corresponding author. E-mail address: [email protected] (A.P. Yunus).

http://dx.doi.org/10.1016/j.rsase.2015.09.002 2352-9385/& 2015 Elsevier B.V. All rights reserved.

red tides can occur at the same time and place each year; or appears randomly (Anderson, 1997). Blooms of red tides are notable for causing mass mortalities of fish, bird, and marine mammals, thus are of national concern due to their shattering damages to fish aquaculture and associated economies (Richlen et al., 2010). Although the problem has been identified for longtime, researchers and government agencies have been slow to investigate possible control strategies of algal blooms (Anderson, 1997). However in recent years, the topic has received considerable attention from various agencies, and the scientific community into the management and control policies of HABs, mainly because of the concern on increasing population who are depending on the marine resources and recent reports on climate change scenarios. Effective detection and timely observation of HABs is critical for ecological modeling and

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management of red tide impacts. Needless to say, precise monitoring of red tides are time and cost consuming. Significant studies have been dedicated to red tide observation, but most of which relies with conventional in-situ ship surveys and buoy stations. Water sample collection and analysis under controlled lab environments are highly laborious process and therefore the frequency and spatial scale of such measurements are limited. For the same reason, satellite measurements have found to be more effective in identification and monitoring of HABs, thanks to their high spatial and temporal coverage over large scales (Zhao and Ghedira, 2014). Today, dedicated ocean color sensors that provide synoptic coverage of world's vast ocean are operating at daily frequencies. These sensors are continually monitoring the waters unreachable for humans (eg. Jena et al., 2012; Sravanthi et al., 2013). Dedicated wave length bands in Sea-viewing Wide Field-of-view Sensor (SeaWiFS), Moderate Resolution Imaging Spectroradiometer sensors (MODIS) abroad aqua and terra, Ocean Color Monitor I and II abroad OceanSAT and Landsat series are made available to the scientific community by respective space agencies, revolutionized deeper understanding of our biological oceanography. Because the color of oceanic waters is directly related to the substances and organisms within it, the spectral water leaving radiances data provided by the ocean color sensor is a combined result of those substances such as suspended sediments, phytoplankton, colored dissolved organic matter (CDOM) and bottom reflectance (Hu et al., 2005; Platt, 2008; Avinash et al., 2012). Several authors have shown the successful usage of water leaving radiances from ocean color data for mapping the planktonic concentration by measuring chlorophyll-a (Chl-a)in a wide variety of oceanic waters (e.g. O'Reilly et al., 1998; Doña et al., 2015). In Case I waters, Chl-a algorithms are based on single bands or band ratios but for Case II waters (see IOCCG, 2000. for definitions of Case 1 and Case 2 waters), band ratios are often used to retrieve the Chl-a (Tyler et al., 2006; Tebbs et al., 2013). Oceanographers today are familiar with the mapping and monitoring of remotelysensed chlorophyll from a range of optical sensors (Sathyendranath et al., 2014). Anomalies observed in the Chl-a concentrations are used as indices of HABs (Stumpf et al., 2003; Svendsen et al., 2004). Svendsen et al. (2004), in Scandinavian waters monitored the real-time algal blooms of 2001 using Chl-a measurements from SeaWiFS data. Tomlinson et al. (2004) mapped the HABs off Florida coast using the same method with some success. However, difficulties are reported for this technique (Hu et al., 2005); such as (a) uncertainties in the atmospheric correction models (b) bio-optical algorithms to retrieve the Chl-a concentrations from different case waters (c) difficulties in determining whether high chlorophylls are related to HABs or not. Over the time, sophisticated atmospheric correction models are developed, however it remains difficult to accurately quantify the Chl-a concentration in different oceanic waters with a single bio-optical algorithm, as chlorophyll levels do not always matches the threshold given for a particular water. Moreover, new sensors are put in to earth's orbit every year with the

advancement in space technology. Hence, development of bio-optical algorithms for Chl-a retrieval from different sensors and in regional level still require significant research. The HABs are frequent in the Japanese coastal waters. Richlen et al. (2010) reported that a marine toxic plankton called Cochlodinium polykrikoides causes notable mortality of farmed fish in Japanese waters. Frequent occurrences of red tide in Tokyo Bay have been observed since 1950s (Marumo and Murano, 1973). In Tokyo Bay, large amount of dissolved nitrogen and phosphorous are present that originated from its catchment rivers, becomes nutrients for planktons (TMGBE, 2015). Because of longer day light hours during the period between spring and autumn, the day time temperature increases and are favorable conditions to grow the phytoplankton. Terada et al. (1974) reported that the chlorophyll concentration in the Tokyo Bay is significantly higher than other inshore regions in Japan. Marumo and Murano (1973) and others (Marumo et al., 1974; Han and Furuya, 2000) studied the diatom succession of plankton in Tokyo Bay and reported that water is slightly eutrophic. The country thus initiated a broad evaluation of bloom-control strategies following these studies. However, the problem remains there; high concentrations of the Chl-a have been reported during the month of July and August in this area from in-situ observations (Suzumura et al., 2004). Their studies also reported lower concentrations of Chl-a during the winter months. It was concluded by them that the lowering water temperature during the winter months is the reason for low Chl-a rather than insufficient nutrient availability. Bacillariophyceae, Cochlodinium catenatum, Heterosigma akashiwo, and Skeletenema costatum are major marine planktons identified in Tokyo Bay that cause algal blooms (Suzumura et al., 2004; Matusuoka et al., 2008; TMGBE, 2015) Although many studies have examined the morphology and taxonomy of HABs (Tsuji et al., 1974; Matsuoka, 1999; Matsuoka et al., 2008; Nakane et al., 2008) and water quality in terms of the chlorophyll concentrations (Suzumure et al., 2004; Nakane et al., 2008) in the Tokyo Bay, routine water quality sampling and analysis has been conducted only during clear weather periods. (Maki et al., 2007). Therefore, information gathering in a large spatial scale and continuous observation on the aquatic environment of Tokyo Bay is still lacking. Moreover, it is difficult to accurately estimate the daily water quality parameter other than the four stations maintained by the Ministry of Land, Infrastructure and Transport Bureau (MLIT). The objective of our study thus are manifolds: (i) Development of a regional bio-optic algorithm for Tokyo Bay using Landsat 8 OLI image.s (ii) Evaluation of surface reflectance product of Landsat 8 OLI. (iii) Monitoring the seasonal variation in chlorophyll-a concentrations in Tokyo Bay. (iv) Identification of red tides in Tokyo Bay using Landsat images.

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Fig. 1. Location of the study area, and four sampling sites in the Tokyo maintained by the Ministry of Land, Infrastructure and Transport Bureau.

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Table 1 Basic information of Tokyo Bay (Ministry of Land Infrastructure, Transport and Port Authority, Japan – 2006). Water surface area (km2)

Bay mouth Average depth (m) width (km)

Volume (billion cubic meter)

Number of ports

1380

20.9

621

4

38.6

2. Tokyo Bay This study focuses on the waters of Tokyo Bay, the large semi-enclosed coastal sea, off the east coast of Japan (Fig. 1). The hinterland of the bay is one of the most populated and industrialized area in the world. The bay is surrounded in three sides by Chiba, Kanagawa prefecture and Tokyo metropolitan. The average depth of the bay is about 38.6 m and area covers about 1380 km2 (Table 1). Twelve river's drain into the Tokyo Bay, of which major rivers are Arakawa River, the Tamagawa River, Sumidagawa River, and the Edogawa River. Large amount (about 4.7 km3) of industrial and domestic sewage effluents are discharging each year into the Tokyo Bay through these rivers (Hashimoto et al., 1998). Effluents released into the bay water is highly contaminated and poses risk of pathogen and human diseases (Islam, 2009). During rainfall and storm surges, more and more effluents are released into the bay without treated at the sewage treatment plants (STP), because the handling capacity of STP exceeds during the heavy rainfall and thus causes increased risk to the marine population. The catchment area of the Tokyo Bay is about 9216 km2 and the land area is 37,7907 km2 (TBEIC, 2015). Rainfall and runoff rates are highly seasonal with maximum occurring during the months of July and August. Tokyo Bay also experiencing frequent storms and runoff events in typhoon seasons of September and October. The surface water in Tokyo Bay is turbid due to high concentration of particulate organic matter that is well correlated with chlorophyll-a (Andou and Shimazu, 1991). The organic matter is supplied from the major rivers flowing in this region, but the amount is much smaller than that generated in the bay (Ogawa and Ogura, 1990). Kawabe and Kawabe (1997) reported that great deal of the particulate organic matter is generated within the bay by phytoplankton bioactivities. Their study also reported that COD in Tokyo Bay is highly correlated with chlorophyll-a and phaeo pigments. Suspended sediments in the bay is mostly composed of land-derived and autochthonous materials (Suzumura et al., 2004). Their abundance were found near the river's mouth and shallow waters (o2 m deep). It is believed that the re-suspension of bottom sediments and physical turbulence caused high concentration of SPM in shallow waters. Since the water is highly turbid at shallower depths, and the average depth of the bay area is relatively high (38.6 m), the reflectance from the bottom of the bay may be neglected. The chlorophyll measurements from the above studies suggest that cyanobacterial biomass is the dominant factor affecting light penetration in the inner Tokyo Bay.

3. Material and methods 3.1. Water quality data The Ministry of Land, Infrastructure and Transport Bureau has installed four real-time observation stations for measuring water quality in the Tokyo Bay. Location of the stations are shown in Fig. 1. We obtained the in-situ water quality data such as Chl-a, total suspended solids (TSS), and dissolved oxygen (DO) from Tokyo Bay Environmental Information Center (TBEIC) web archives maintained by the MLIT (TBEIC, 2015). The data from the real-time observation stations are automatically updating every one hour and are measuring from upper surface layer, middle surface layer and bottom surface layer within the bay region (TBEIC, 2015). Hourly temperature, salinity and wind speed information are also available in TBEIC archives for the four sites. We used the TBEIC data for Chla that corresponding to the Landsat – 8 OLI passes between January 2014 and December 2014, and analyzed the upper surface water quality measurements that corresponds to 1–2.3 m below the surface. Statistics describing the variation of Chl-a measured and other parameters during the study period are shown in Table 2 and Appendix Table A1. Chl-a concentrations varies between 0.3 and 137.0 μg/l. The minimum value (0.3 μg/l) and the maximum concentration of Chl-a (137 μg/l) are found for the site no. 4 on the day of 31st of May and 16th of June 2014. Turbidity values ranges between 0.9 and 7.5 NTU. The water temperature during the study period ranges between 8° and 30 °C. Maximum salinity ranges (430) corresponds to the winter months (November–March). Dissolved oxygen content varies between 4.23 and 16.62 mg/l; the minimum is found in the month of May and maximum in the month of July for the site 3 and 1 respectively. 3.2. Landsat 8 OLI Two sets of Landsat 8 OLI images of Tokyo Bay (Path 107, Row 035) were downloaded from the USGS Earth Explorer http://earthexplorer.usgs.gov. The first set of images are Level 1T processed, meaning that they have undergone geometric correction and terrain calibration but not undergone atmospheric correction. The images are selected and downloaded in such a way that each scenes contain less than 20% cloud cover. Although this is the case, before doing any spectral analysis with optical imagery, atmospheric correction should be carried (EXELIS, 2015). Therefore, we processed all the downloaded Level 1T scenes for atmospheric correction using ENVI image analysis software. It has been noted that for the relatively clear scenes, a reduction in between-scene variability can be achieved through a normalization for solar irradiance by converting spectral radiance to planetary reflectance or albedo (USGS, 2015a). For performing atmospheric correction, the uncalibrated digital numbers (DN) from Level T1 scenes for each band of OLI was first converted to dimensionless top-of-atmosphere (TOA) radiances and

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Table 2 Descriptive statistics of water quality parameter measured for the 4 sites of TBEIC during the study period (TBEIC, 2015). Parameter

Min

Max

Mean

Std. dev

Depth of measurement (m) Water temperature (°C) Salinity DO (mg/l) Chl-a (μg/l) Turbidity (NTU)

1.01 8.29 21.08 4.23 0.3 0.9

2.29 29.89 32.95 16.62 137.2 7.5

1.25 18.55 29 9.56 32.32 2.75

0.32 6.77 3.17 2.78 32.19 1.64

reflectance values using the following functions: Lλ ¼ Ml Q cal þ Al

ρTOA ¼ Mρ Q cal þ Aρ where Lλ is spectral radiance, ML is band specific multiplicative rescaling factor (RADIANCE_MULT_BAND_X), AL is band specific additive rescaling factor (RADIANCE_ADD_BAND_X), ρTOA is the planetary reflectance, Mρ is the band specific multiplicative rescaling factor (REFLECTANCE_MULT_BAND_X), Aρ is the band specific additive rescaling factor (REFLECTANCE _ADD_BAND_X), and Qcal is the quantized and calibrated standard product pixel values (DN). TOA reflectance where next corrected for the sun angle. Then the atmospheric correction is carried out using dark object subtraction (DOS) method. DOS is based on the assumption that within the image, some pixels are in complete shadow and their radiances received at the satellite are due to the path radiance (Chavez, 1996). It is noted that the accuracy of DOS method is lower than physical based correction, but are very useful when no atmospheric measurements are available. The path radiance Lp is given by (Sobrino et al., 2004): Lp ¼ Lmin  LDO1% where Lmin is the “radiance that corresponds to a digital count value for which the sum of all the pixels with digital counts lower or equal to this value is equal to the 0.01% of all the pixels from the image considered” (Sobrino et al., 2004), and LDO1% is the radiance of dark object. The surface reflectance is thus computed using the given formula. h i
 π  Lλ Lp  d2  ρ¼  ESUNλ  cos θs The second set of images is called Provisional Landsat 8 Surface Reflectance (L8SR) product which was generated using a specialized software (USGS, 2015b). The L8SR were ordered through the Earth Explorer on demand service. These products have undergone in-house atmospheric correction along with the geometric correction and terrain calibration (USGS, 2015b). Currently Landsat 8 scenes acquired from April 2013 through December 2014 are available as TOA and Surface Reflectance (SR) products in the Earth Explorer. We downloaded the TOA and SR scenes for the Tokyo Bay between 2014 January and December. Since L8SR is atmospherically corrected, we did not perform any further correction to this dataset. It is noted that L8SR algorithm is not carried for the scenes with solar zenith angle greater than 72° (USGS, 2015b). Thus we have

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Table 3 Landsat 8 OLI scenes used in this study. Sl. no.

Landsat 8 OLI: Path 107 Row 035

Date and time (JST)

1 2 3 4 5 6 7 8 9 10 11 12

LC81070352014007LGN00 LC81070352014023LGN00 LC81070352014071LGN00 LC81070352014087LGN00 LC81070352014151LGN00 LC81070352014167LGN00 LC81070352014183LGN00 LC81070352014215LGN00 LC81070352014231LGN00 LC81070352014311LGN00 LC81070352014327LGN00 LC81070352014343LGN00

2014-01-07, 10:17:08 2014-01-23, 10:16:57 2014-03-12, 10:16:22 2014-03-28, 10:16:04 2014-05-31, 10:15:25 2014-06-16, 10:15:32 2014-07-02, 10:15:36 2014-08-03, 10:15:49 2014-08-19, 10:16:03 2014-11-07, 10:16:57 2014-11-23, 10:15:59 2014-12-09, 10:15:56

cross checked the downloaded scenes and verified them for further analysis. Twelve cloud free images (o20%) of Tokyo Bay were obtained between the January 2014 and December 2014 (Table 3). These periods match up with the period of the availability of L8SR images. In-situ Chl-a observations that matches with the downloaded Landsat 8 OLI were extracted from the TBEIC archives. Although hourly data is available for all the sites during the study period, we used the 10:00 AM JST data that corresponds to the Landsat -8 satellite overpass ( 10:15 AM) over this region. Thus the satellite and ground data were corresponded within the timeframe which is set by the natural variation of the process being measured (Robinson, 2004; Jena et al., 2010). Surface reflectance DN from Level 1T OLI, surface reflectance (SR) and TOA DN values from L8SR products were then extracted for all the dates for each sampling locations maintained by the MLIT in a GIS environment. Reflectance value for the Kawasaki Island sampling site is extracted based on the 3  3 pixel array at the very nearest point adjacent to the island, because the direct extraction yields the reflectance value of the roof of this artificial land rather than the water surface. Regression analysis was then performed to determine the best fit band or bands that represent the predictor of Chl-a concentration. The insitu data obtained from the observational sites for May 31st 2014 is anomalously low and thus not included in our analysis. 3.3. Hotspot analysis for red tide detection To detect the red tides from high chlorophyll concentration region, we tested the hotspot analysis. Anselin (1995) reported that the spatial data analysis techniques can identify the spatial association and autocorrelation in ortho-referenced images. One such measure of spatial autocorrelation is Moran's I (Moran, 1950). Kim et al. (2009) extracted red tide pixels from MODIS chlorophyll data based on Moran's I method in coastal waters of Korea. However, it is now widely accepted that the local patterns of influence to describe the spatial distribution can effectively performed from local statistical methods that commonly termed as LISA methods – Local Influence of Spatial Autocorrelation (Anselin, 1995; Getis and Ord, 1996). This method is applied locally and are useful in identifying the

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investigation, and (iv) chlorophyll concentration is more than 50 mg/m3. Based on the above criteria 19 events are reported as red tide between April and November for the year 2014 (see Appendix A). Out of the 19 events, three are corresponding with the Landsat 8 OLI cloud free data that we procured (May 31st, July 2nd, and August 19th 2014).

4. Results and discussion 4.1. Regional bio-optic algorithm

Fig. 2. Flow chart that describes methodology for detecting red tides from satellite data using Chl-a and hotspot analysis.

existence of local spatial clustering or ‘‘hot spots’’ (McCullagh, 2006; Pérez‐Peña et al., 2009; Ratcliffe, 2010). The Hot Spot Analysis calculates the Getis-Ord Gi* (Gi) statistic for feature in a weighted set of features. Given a set of weighted data points, the Gi statistic identifies the clusters of points with values higher in magnitude and tells whether features with high values or features with low values tend to cluster in a study area. In Gi statistics, if a feature's value is high, and the values for all of it's neighboring features is also high, it is a part of a hot spot. The Getis-Ord Gi* is defined as: X X Gi ¼ wij ðdÞxj C xj j

j

where wij(d) are the elements of the contiguity matrix for distance d. The matrix assigns a spatial weight for each point pair within a distance d of i. The resultant Gi statistic is in the form of a statistically significant Z score. The larger the Z score is, the more intense the clustering of high values. Detailed procedures for deriving red tides from satellite data using Chl-a and hotspot model are demonstrated in Fig. 2. 3.4. Red tide events in Tokyo Bay Water quality survey for the inner Tokyo Bay has been carried out regularly by the Tokyo Metropolitan Bureau of Environment (TMGBE). The reports are published and available online at http://www.kankyo.metro.tokyo.jp/.The Red tide report for the fiscal year 2014 provided by the TMGBE was used in this study, which includes red tide outbreak date, location and name of dominant species (http://www. kankyo.metro.tokyo.jp/water/tokyo_bay/red_tide/index. html). According to the TMGBE, the criteria for reporting a particular event as red tide is; (i) the color of sea water become brownish, (ii) transparency reduced to 1.5 m or less, (iii) presence of red tide plankton under a microscopic

This section describes the development and results of a bio-optical algorithm for the estimation of Chl-a in Tokyo Bay. To develop the chlorophyll-a algorithm, regression analysis was performed on the in-situ measurements and remote sensing reflectance (Rrs) values. Temporal Chl-a data corresponding to the cloud free Landsat OLI images from the four sites in the Bay included a total of 42 values. Sampling points on 31st of May 2014 was removed due to its very low values of measured Chl-a concentration (0.3–5 μg/l). Based on the remaining samples for the dates corresponds to the Landsat data, the linear, polynomial, cubic, power and exponential models of Chl-a were tested against single band as well as their band ratios. The surface reflectance bands from DOS method, surface reflectance bands from L8SR and the TOA reflectance from L8SR are used with the combination of in-situ data. Then determination coefficients (R2) and estimated standard errors (SE) of the regression models were compared to select the best-fitting data, considering different bands or their ratios. Coefficient of determinations and SE for single band regressions on Landsat OLI first 6 bands are given in Table 4. The best models selected from the best fitting ones are shown in Fig. 3. All correlation for single band reflectance values were found to be positive; because, at increased Chl-a concentration, scattering also increases (Tebbs et al., 2013). The best correlated wavelength with in-situ Chl-a in L8SR surface reflectance bands and TOA bands is for near infra-red band (band 5, R2 ¼0.42 and 0.41 respectively, p o0.05) (Fig. 3a, b). The results also confirmed that band 3 (green) from the DOS corrected reflectance of Level 1T data gives the highest correlation in linear regression (R2 ¼0.62, po0.05, n¼38) (Fig. 3c). The R2 values for DOS reflectance in the first five bands of Landsat OLI (visible spectrum) is found higher than the L8SR surface reflectance data (Table 4). Based on the results, the best single band algorithms from regression model with goodness of fit between chlorophyll-a (ranges between 2 and 137 μg/l) and the Rrs are:   TOA R2 ¼ 0:41 : Chla ¼ ð1559:3  Rrs ðB5ÞÞ  13:29   DOS R2 ¼ 0:62 :

Chla ¼ ð1319:3  Rrs ðB3ÞÞ  34:47

  L8SR R2 ¼ 0:42 :

Chla ¼ ð1472:9  Rrs ðB5ÞÞ 0:428

The best fit quadratic function among the three reflectance data belongs also to the DOS reflectance

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(R2 ¼0.67):   Chla ¼ 18548  Rrs ðB3Þ2  ð841:84  Rrs ðB3ÞÞ þ 21:173 The regression equations for the best band ratio algorithm was obtained from the ratios of blue and green Table 4 Linear coefficient of determination (R2), standard error (SE), and p-values for single band algorithms based on Landsat OLI (n¼ 38). Bands TOA

B1 B2 B3 B4 B5 B6 B7

DOS

L8SR

R2

SE

p

R2

SE

p

R2

SE

p

0.003 0.03 0.25 0.35 0.41 0.34 0.30

32.69 32.21 28.21 26.37 25.11 26.41 27.22

0.72 0.27 0.05 0.05 0.05 0.05 0.05

0.61 0.61 0.62 0.61 0.58 0.30 0.48

20.24 20.43 19.94 20.23 21.10 27.29 23.47

0.05 0.05 0.05 0.05 0.05 0.05 0.05

0.18 0.19 0.33 0.39 0.42 0.34 0.30

29.57 29.37 26.65 25.41 24.77 26.51 27.28

0.05 0.05 0.05 0.05 0.05 0.05 0.05

17

bands of DOS reflectance (R2 ¼0.63, p o0.05). Previous studies also reached reasonable consensus regarding Chl-a derived from Rrs at blue and green band (Ruddick et al., 2003). The algorithm is follows:  2 !   B2 B2 þ 2631:6 Chla ¼ 1020  Rrs  3268:3  Rrs B3 B3 Although the quadratic function of single band (B3) of DOS outperform slightly the band ratio (B2/B3) model in the regression analysis, the latter was applied to the Landsat 8 OLI images for mapping the Chl-a concentration and interpreting the seasonal cycle. This is because of the band ratio model's ability to maximize the effects of distinctive optical features. It is not novel that the blue-green ratios obtained good results for estimating Chl-a. In previous studies also, the blue-green ratios was successfully tested for retrieving Chl-a (Morel and Antoine, 2007). OC4 global algorithm that applied in SEADAS for global ocean Chl-a retrieval also uses a fourth order polynomial function of blue-green ratios (O'Reilly et al., 1998).

Fig. 3. Scatter plots showing R2 values and best fit regression lines between Chl-a, and Rrs derived from (a) L8SR Top of Atmosphere (TOA) product, (b) L8SR surface reflectance product, and surface reflectance from Level 1T (c, d).

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Fig. 4. Chl-a retrieved from the Landsat OLI images captured between January and December 2014 for the Tokyo Bay.

4.2. Seasonal chlorophyll-a concentrations in Tokyo Bay Maps of Chl-a concentrations derived from the Landsat OLI images for the Tokyo Bay shows considerable seasonal

variation during the study period (Fig. 4). From the Chl-a maps, the mean and standard deviation in Chl-a across the Tokyo Bay for the study period is noted. The observed Chla for the month of January in the most part of the bay is

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less than 20 μg/l (Fig. 4a and b). High concentrations of Chl-a is found in March with a mean value of 38 μg/l and standard deviation of 18 μg/l (Fig. 4c, d). Observations found during the months of May, June, July, and August has much higher Chl-a concentrations (Fig. 4e–i). The mean values of Chl-a for May 31st, June 16th, July 2nd, August 3rd, and August 19th are 51.2 μg/l, 82.65 μg/l, 50.63 μg/l, 58.93 μg/l, and 35.10 μg/l respectively. The concentrations decreased significantly in the months of November and December (Fig. 4j–l). The Chl-a observed for November 23 and December 9 are 17.30 μg/l and 16.77 μg/l respectively. In general, the observation of the Fig. 4 demonstrate that the Chl-a concentrations during summer months are associated with high phytoplankton activity, and that for winter months are associated with low phytoplankton activity in the Tokyo Bay. Similar observation is also noted from the field studies of Suzumura et al., 2004 in the inner Tokyo Bay. Recalling the results of their studies, the low concentration of Chl-a in Tokyo Bay did not result from insufficient nutrient availability, but because of the varying water temperature. The surface temperature (ST) and water temperatures (WT) were low in January–March and November–December (1.91–5.16 °C ST 8.29–16.13 °C WT) whereas the same for May–August is relatively higher (20.79–28.37 °C ST and 20.77–29.892 °C WT). Our study thus confirms the results of Suzumura et al. (2004) that the temperature is an important factor affecting the Chl-a concentration in the Tokyo Bay. The spatial distribution of Chl-a values shows that their concentrations were generally higher near the coast and in the mouth of the rivers (Fig. 4). Peak summer Chl-a observed for the river mouths in the Bay are greater than 90 μg/l, whereas those in the winter months did not exceed 60 μg/l. Minimum Chl-a values in the summer periods were less than 20 μg/l and are concentrated in the central interior bay. These observations are in agreement with results obtained by previous researches in Tokyo Bay (Shibata and Aruga, 1982; Kishino et al., 2005). 4.3. Red tide detection in the Tokyo Bay Red tide detection using chlorophyll algorithms usually involve the generalized relationship between a high chlorophyll content and HAB occurrences. However, red tides do not always necessarily occur in all high chlorophyll zones (Kim et al., 2009). This section describes how a combination of field data and Getis-Ord Gi* statistics can detect the presence of red tide in high chlorophyll zones. In this study, the detection of red tides is based on the assumption that a pixel with a significantly high value of chlorophyll is interesting but may not be a red tide. To be detect Chl-a pixel as a red tide pixel, the pixel must have a significantly high value of Chl-a and be surrounded by other pixels with high values as well. With this assumption, the hotspot analysis tool calculates the Getis-Ord Gi* statistic for each pixels in the Chl-a data retrieved from Landsat OLI images for January to December. As noted in Section 3.2, the results of hotspot analysis is z scores; the larger the z score is, the more intense the clustering of high values. The results of z score for the study period are mapped and shown in Fig. 5.

19

Maps of z scores produced from Chl-a shows that clusters of high z scores are occurred for the following dates of Landsat OLI images; March 12th, May 31st, July 2nd, and August 19th 2014 (Fig. 5). The remaining days are mapped with low z scores (January 7th, January 23rd, March 28th, August 3rd, November 23rd, and December 9th 2014; the dates of November 7th and June 16th were not included in the analysis because of partial cloud cover). The spatial distribution of high z score occur largely near the coast and at the mouth of the major rivers. We compared the z score results with the red tide field report of TMGBE 2014 FY (Appendix A). The comparison shows that the dates of high z score clustering (red color in Fig. 5) always well corresponds to the red tide events and locations identified by the field data by the TMGBE. According to the TMGBE report, S. costatum and Ditylum brightwellii was the dominant species on August 19th event; S. costatum and H. akashiwo was the dominant species on July 2nd event; H. akashiwo, S. costatum and Noctiluca scintillans was the dominant species on May 31st event (measurement on June 3rd). For the dates with no significant z scores, there is no red tide events reported. 4.4. Implication of the research Considerable field observations have been made by the respective government agencies and researchers to better understand the algal blooms in waters off the Tokyo Bay. In July–August, rapid growth and outbreaks of HABs were observed in the coastal waters, in which the dominant species is S. costatum. The in-situ measurements were only performed during clear sky conditions that limit the continuity in their observations. The satellite based observation however provide continuity in terms of both spatial and temporal resolutions. The remote sensing algorithms that is suitable for retrieving Chl-a in coastal waters, can also be used to detect red tide event because of a significant association of plankton pigments. Several previous studies with some success, mapped red tide out breaks using Chl-a as an index from various sensors around the globe (Ahn and Shanmugam, 2006; Ahn et al., 2006; Kim et al., 2009; Hao et al., 2011; Zhao and Ghedira, 2014). This study evaluated the L8SR reflectance product available from the USGS with the traditional DOS atmospheric correction for mapping the Chl-a in coastal waters of Tokyo Bay. The results presented in this study indicate that L8SR reflectance products suffers the artifacts mentioned in the L8SR user guide that “the efficiency of L8SR correction will be likely reduced in places where atmospheric correction is affected by diverse conditions” such as in coastal regions thus reflexing limited accountability for ocean color bio-physical mapping. It is worth to mention that L8SR is released as a provisional products and the values are subject to change (L8SR, 2015). The relationships obtained from the reflectance between the blue-green bands or near infraredred and Chl-a are what would be expected theoretically (e.g. Morel and Antoine, 2007; Tebbs et al., 2013). Thus, we expected that our model based on the band ratios of bluegreen reflectance from DOS (R2 ¼0.63) of Landsat OLI, would be most successful in estimating the Chl-a values in Tokyo Bay. The models based on near infrared-red however not given a reasonable fit with our data. Hence, we mapped the

20

A.P. Yunus et al. / Remote Sensing Applications: Society and Environment 2 (2015) 11–25

Fig. 5. Getis-ORD Gi* statistics based z score mapped from the Chl-a maps for the study period. The red colors shown are possible red tide breakout locations. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

Chl-a in Tokyo Bay for detecting the red tides with blue-green ratios. Although the atmospheric correction applied in this study is not perfect, one must note that the time difference between the in-situ data and the images selected is only 15 minutes, thanks to the real time observation products of TBIA, thus minimized the errors attributed in the bio-optical model. The accurate atmospheric correction parameters applying to remote sensing images is still a puzzle (Li et al., 2012; Tebbs et al., 2013; Shi et al., 2014). The red tide pixel detection using remote sensing measurements and instituting the dependability of such retrievals are challenging, largely because of the limited field data availability for comparison (Carvalho et al., 2010). In this study,

the use of the in-situ red tide database maintained by the TMGBE successfully achieved the purpose of comparison. The simple model based on Getis-Ord Gi* hotspot analysis proposed in this study could distinguish between red tide pixels from Chl-a based on the z scores. The red tide pixels identified in this study coincides with the dates of red tide events observed by the TMGBE. The results of Gi analysis confirmed our earlier assumption (see Section 4.3). The strength of the proposed model lies in the simplicity of the approach. However, it must be emphasized that the simple model may possibly mislead in case of a significant Chl-a bloom which is not a red tide. This problem might be solved by more in-situ data collection during red tide event and input them in the

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developmental stage of model, whereas, algorithm accuracy might also be improved. For the further development of the model, a threshold value for z score could be calculated and such a schema would assist researchers to investigate Tokyo Bay red tides in a focused manner. Suzumura et al. (2004) and others noted that the seasonal variation in the Chl-a concentration in Tokyo Bay is accounted for the variability in water temperature. The summer algal blooms and temperature are always well correlated and the same is observed for most of the coastal and lake waters around globe (e.g. Shi et al., 2014). Does the temperature only the most influencing factors for algal blooms in Tokyo Bay.? The reports of TMGBE red tides and results from this study however indicates temperature is not the only influencing factor. It is noted that, before the red tide events, rainfall was common. About 19.5 mm of rainfall was recorded five days before the August 19th event, 48 mm of rainfall recorded two days before the July 2nd event and 29 mm of rainfall recorded four days before the May 31st event. The Figs. 4 and 5 shows that the Chl-a concentrations is higher in the mouth of the rivers and near the coast. These figures and the report all suggest that the major rivers of the Tokyo carries enormous amount of nutrients to the bay and causes a major influencing factor for the occurrence of algal blooms.

model is proposed for the retrieval of Chl-a based on band 2 and band 3 of the DOS corrected Landsat 8 Level 1Tproduct (R2 ¼0.63). Considerable seasonal variation is observed during the study period; in which winter month's records low biomass activity whereas summer month's records high biomass activity. The simple statistical model proposed using Geti-Ord Gi* statistics detect red tide hot spots for the days of March 12th, May 31st, July 2nd, and August 19th of 2014. These dates except for March 12th coincides with the red tide events recorded from in-situ observations. The simple model lays foundation for obtaining the spatiotemporal distribution information of Chl-a and red tide events from the freely available Landsat 8 OLI data. Thus the outcome of this work will contribute to our understanding, and management of Tokyo Bay.

Acknowledgments Authors thanks USGS for Landsat scenes, TBEIC for water quality data, and TMGBE for red tide reports. The authors also wish to acknowledge the editor and reviewer for their comments to improve the quality of this manuscript.

5. Conclusion Appendix A Remote Sensing of Chlorophyll-a in 2014 in Tokyo Bay was extensively studied using Landsat OLI images. A numerical

21

See Tables A1 and A2 and Fig. A1

22

A.P. Yunus et al. / Remote Sensing Applications: Society and Environment 2 (2015) 11–25

Table A1 Physical parameters including Chl-a measured by TBEIC in the four sites of Tokyo Bay during the study period. FID 42011 0 1 2 3 42027 4 5 6 7 42075 8 9 10 11 42091 12 13 14 15 42155 16 17 18 19 42171 20 21 22 23 42187 24 25 26 27 42219 28 29 30 31 42235 32 33 34 35 42315 36 37 38 39 42331 40 41 42 43 42347 44 45 46 47

Location

Temperature (°C)

Salinity

DO (%)

DO (mg/L)

Chl-a

NTU

Port of Chiba Kawasaki Island Urayasu Chiba Mouth

11.09

32.92

103.7

9.26

19.2

1.7

10.62

31.88

106.5

9.68

32.5

1.5

Port of Chiba Kawasaki Island Urayasu Chiba Mouth

9.01 10.05 8.29 10.04

31.85 32.95 31.15 32.21

113.6 104.7 111.2 120.4

10.7 9.56 10.69 11.05

22.1 28.2 24.2 22.8

2.6 1.7 2.9 2.4

Port of Chiba Kawasaki Island Urayasu Chiba Mouth

10.06

31.56

126.4

11.65

11.5

2

10

31.23

118.5

10.96

13.1

1.6

Port of Chiba Kawasaki Island Urayasu Chiba Mouth

12.6

30.59

143.3

12.59

21.5

1.9

13.29 11.67

30.41 31.8

147 117.2

12.74 10.42

67.1 17.4

4.8 3.4

Port of Chiba Kawasaki Island Urayasu Chiba Mouth

22.5 20.77 23.22 22.35

27.77 31.31 27.11 28.11

101.1 99.1 57.9 95.4

7.46 7.39 4.23 7.04

2.2 3.8 5 0.3

0.9 1.6 3.2 1

Port of Chiba Kawasaki Island Urayasu Chiba Mouth

22.2 25.18 22.74

25.43 22.71 21.08

155.4 205.1 208.5

11.68 14.85 15.91

98.9 93.7 137.5

3.9 7.5 4.7

Port of Chiba Kawasaki Island Urayasu Chiba Mouth

25.44 24.25 25.38 24.67

27.5 22.49 25.2 25.1

237 154.5 100.3 212.5

16.62 11.38 7.14 15.31

48.4 61.7 45.5 70.2

4.9 1.9 6 1.4

Port of Chiba Kawasaki Island Urayasu Chiba Mouth

29.89 28.02 29.89 27.48

25.22 27.54 25.22 26.93

159.8 133.1 159.8 117.6

10.53 8.94 10.53 7.99

71.9 59.1 71.9 48.1

6.3 2.1 6.3 2.4

Port of Chiba Kawasaki Island Urayasu Chiba Mouth

27.81 27.09 27.81 27.61

25.28 28.43 25.28 27.39

152.8 87.4 152.8 120.9

10.42 5.93 10.42 8.18

62.4 14.7 62.4 28.5

3.3 2.3 3.3 5.2

Port of Chiba Kawasaki Island Urayasu Chiba Mouth

18.71 18.39 18.71 18.21

31.33 31.47 31.33 30.39

87.2 101.4 87.2 109.1

6.75 7.89 6.75 8.58

6.2 22.7 6.2 25.2

2.3 1.8 2.3 1.6

Port of Chiba Kawasaki Island Urayasu Chiba Mouth

15.97 16.13 15.97 15.82

31.27 32.32 31.27 30.97

80.6 87.9 80.6 95.4

6.58 7.11 6.58 7.83

4 2.8 4 6.4

1.8 1.3 1.8 1

Port of Chiba Kawasaki Island Urayasu Chiba Mouth

13.78 13.98 13.78 13.2

30.73 31.58 30.73 31.07

95.4 88.8 95.4 96.8

8.17 7.53 8.17 8.37

4.3 2.9 4.3 2.8

2.1 1.3 2.1 1.6

Table A2 Field survey dates and Red Tide outbreak confirmations in 2014 FY.Source: http://www.kankyo.metro.tokyo.jp/water/tokyo_bay/red_tide/index.htmlH26akashio

A.P. Yunus et al. / Remote Sensing Applications: Society and Environment 2 (2015) 11–25 23

24

A.P. Yunus et al. / Remote Sensing Applications: Society and Environment 2 (2015) 11–25

Fig. A1. Red tides photographed by TBIA in Tokyo Bay.

(Source-http://www.kankyo.metro.tokyo.jp/water/tokyo_bay/red_tide/index.htmlH26akashio)

References Ahn, Y., Shanmugam, P., 2006. Detecting the red tide algal blooms from satellite ocean color observations in optically complex Northeast-Asia Coastal waters. Remote Sens. Environ. 103 (4), 419–437. Andou, H., Shimazu, T., 1991. Statistical analysis of water quality data for Tokyo Bay (1). 1991 Annual Report of Environment Science Research Institute of Tokyo Metropolitan Governmen, pp. 119–128. Anderson, D.M., 1997. Turning back the harmful red tide. Nature 388 (6642), 513–514. Anderson, D.M., 1994. Red tides. Sci. Am. 271 (2), 62–68. Anselin, L., 1995. Local indicators of spatial association—LISA. Geogr. Anal. 27 (2), 93–115. Avinash, K., Jena, B., Vinaya, M.S., Jayappa, K.S., Narayana, Ganagadhra Bhat, H., 2012. Regionally tuned algorithm to study the seasonal variation of suspended sediment concentration using IRS-P4 Ocean Color Monitor data. The Egyptian Journal of Remote Sensing and Space Sciences 15 (1), 67–81. Carvalho, G.A., Minnett, P.J., Fleming, L.E., Banzon, V.F., Baringer, W., 2010. Satellite remote sensing of harmful algal blooms: a new multialgorithm method for detecting the Florida Red Tide (Karenia brevis). Harmful Algae 9 (5), 440–448. Chavez, P.S., 1996. Image-based atmospheric corrections-revisited and improved. Photogramm. Eng. Remote Sens. 62 (9), 1025–1035. Doña, C., Chang, N.B., Caselles, V., Sánchez, J.M., Camacho, A., Delegido, J., Vannah, B.W., 2015. Integrated satellite data fusion and mining for monitoring lake water quality status of the Albufera de Valencia in Spain. J. Environ. Manag. 151, 416–426. Du Yoo, Y., Jeong, H.J., Kim, J.S., Kim, T.H., Kim, J.H., Seong, K.A., Lee, S.H., Kang, N.S., Park, J.W., Park, J., 2013. Red tides in Masan Bay, Korea in 2004–2005: II. Daily variations in the abundance of heterotrophic protists and their grazing impact on red-tide organisms. Harmful Algae. 30, S89–S101. EXELIS, 2015. When should I Correct My Imagery For Atmospheric Effects? Blog: Exelis Visual Information Solutions. 〈http://www.exelisvis.com/Company/PressRoom/Blogs/TabId/836/ArtMID/2928/ArticleID/14585/When-Should-I-Correct-My-Imagery-for-AtmosphericEffects.aspx〉 (last accessed 12.09.15). Getis, A., Ord, J.K., 1996. Local spatial statistics: an overview, Spatial Analysis: Modelling in a GIS Environment374. Han, M., Furuya, K., 2000. Size and species-specific primary productivity and community structure of phytoplankton in Tokyo Bay. J. Plankton Res. 22 (7), 1221–1235. Hao, Y., Tang, D., Yu, L., Xing, Q., 2011. Nutrient and chlorophyll a anomaly in red-tide periods of 2003–2008 in Sishili Bay, China. Chin. J. Oceanol. Limnol. 29 (3), 664. Hashimoto, S., Azuma, T., Otsuki, A., 1998. Distribution, sources, and stability of haloacetic acids in Tokyo Bay, Japan. Environ. Toxicol. Chem. 17 (5), 798–805. Hu, C., Muller-Karger, F.E., Taylor, C.J., Carder, K.L., Kelble, C., Johns, E., Heil, C.A., 2005. Red tide detection and tracing using MODIS

fluorescence data: a regional example in SW Florida coastal waters. Remote Sens. Environ. 97 (3), 311–321. IOCCG, 2000. Remote Sensing of Ocean Colour in Coastal, and Other Optically-Complex Waters. Reports of the International Ocean-Colour Coordinating Group (IOCCG), pp. 3. Islam, M.M., 2009. Influence of Rainfall and Combined Sewerage Overflow on Odaiba Area in Tokyo Bay (doctoral dissertation). The University of Tokyo. Jena, B., Swain, D., Tyagi, A., 2010. Application of artificial neural networks for sea-surface wind-speed retrieval from IRS-P4 (MSMR) brightness temperature. Geoscience and Remote Sensing Letters, IEEE 7 (3), 567–571. Jena, B., Swain, D., Avinash, K., 2012. Investigation of the biophysical processes over the oligotrophic waters of South Indian Ocean subtropical gyre, triggered by cyclone Edzani. International Journal of Applied Earth Observation and Geoinformation 18, 49–56. Kawabe, M., Kawabe, M., 1997. Temporal and spatial characteristics of chemical oxygen demand in Tokyo Bay. J. Oceanogr. 53 (1), 19–26. Kim, Y., Byun, Y., Kim, Y., Eo, Y., 2009. Detection of Cochlodinium polykrikoides red tide based on two-stage filtering using MODIS data. Desalination 249 (3), 1171–1179. Kishino, M., Tanaka, A., Ishizaka, J., 2005. Retrieval of Chlorophyll a, suspended solids, and colored dissolved organic matter in Tokyo Bay using ASTER data. Remote Sens.Environ. 99 (1), 66–74. Li, Y., Wang, Q., Wu, C., Zhao, S., Xu, X., Wang, Y., Huang, C., 2012. Estimation of chlorophyll a concentration using NIR/red bands of MERIS and classification procedure in inland turbid water. Geosci. Remote Sens. IEEE Trans. 50 (3), 988–997. Maki, H., Sekiguchi, H., Hiwatari, T., Koshikawa, H., Kohata, K., Yamazaki, M., Kawai, T., Ando, H., Watanabe, M., 2007. Influences of storm water and combined sewage overflow on Tokyo Bay. Environ. Forensics 8 (1-2), 173–180. Marumo, R., Murano, M., 1973. Succession of plankton diatoms in Tokyo Bay. La Mer 11, 70–82. Marumo, R., Sano, A., Murano, M., 1974. Further study on succession of plankton diatoms in Tokyo Bay. La Mer 11, 70–82. Matsuoka, K., Iwataki, M., Kawami, H., 2008. Morphology and taxonomy of chain-forming species of the genus Cochlodinium (Dinophyceae). Harmful Algae 7 (3), 261–270. Matsuoka, K., 1999. Eutrophication process recorded in dinoflagellate cyst assemblages—a case of Yokohama Port, Tokyo Bay, Japan. Sci. Total Environ. 231 (1), 17–35. McCullagh, M.J., 2006. Detecting hotspots in time and space. ISG06. Moran, P.A., 1950. Notes on continuous stochastic phenomena. Biometrika 37, 17–23. Morel, A., Antoine, D., 2007. Pigment index retrieval in case 1 waters. MERIS Level 2 Algorithm Theoretical Basis Document, ATBD2.9, ESA Doc.No. Nakane, T., Nakaka, K., Bouman, H., Platt, T., 2008. Environmental control of short-term variation in the plankton community of inner Tokyo Bay, Japan. Estuar. Coast. Shelf Sci. 78 (4), 796–810.

A.P. Yunus et al. / Remote Sensing Applications: Society and Environment 2 (2015) 11–25

Ogawa, H., Ogura, N., 1990. Source and behavior of organic carbon of seawater in Tokyo Bay. Geochemistry 24, 27–41. Okaichi, T., 2004. Red tides. Springer Science & Business Media, Berlin. O'Reilly, J.E., Maritorena, S., Mitchell, B.G., Siegel, D.A., Carder, K.L., Garver, S.A., Kahru, M., McClain, C., 1998. Ocean color chlorophyll algorithms for SeaWiFS. J. Geophys. Res.: Oceans (1978–2012) 103 (C11), 24937–24953. Pérez‐Peña, J.V., Azañón, J.M., Booth‐Rea, G., Azor, A., Delgado, J., 2009. Differentiating geology and tectonics using a spatial autocorrelation technique for the hypsometric integral. J. Geophys. Res.: Earth Surf. (2003–2012) 114 (F2). Platt, T., 2008. Why Ocean Colour?: The Societal Benefits of Ocean-colour Technology. (Reports of the International Ocean-Colour Coordinating Group (IOCCG)7. Ratcliffe, J., 2010. Crime mapping: spatial and temporal challenges, Anonymous Handbook of Quantitative CriminologySpringer, London 5–24. Richlen, M.L., Morton, S.L., Jamali, E.A., Rajan, A., Anderson, D.M., 2010. The catastrophic 2008–2009 red tide in the Arabian Gulf region, with observations on the identification and phylogeny of the fish-killing dinoflagellate Cochlodinium polykrikoides. Harmful Algae 9 (2), 163–172. Robinson, I.S., 2004. Measuring the oceans from space: the principles and methods of satellite oceanography. Springer Science & Business Media. Ruddick, K., Lacroix, G., Park, Y., Rousseau, V., De Cauwer, V., Debruyn, W., Sterckx, S., 2003. Overview of Ocean Colour: theoretical background, sensors and applicability for the de-tection and monitoring of harmful algae blooms (capabilities and limitations). Sathyendranath, S. Aiken, J., Alvain, S., Barlow, R., Bouman, H., Bracher, A., Brewin, R., Bricaud, A., Brown, C., Ciotti, A., 2014. Phytoplankton Functional Types from Space. Reports of the International OceanColour Coordinating Group (IOCCG) 15, pp.1–156. Shi, K., Zhang, Y., Liu, X., Wang, M., Qin, B., 2014. Remote sensing of diffuse attenuation coefficient of photosynthetically active radiation in Lake Taihu using MERIS data. Remote Sens. Environ. 140, 365–377. Shibata, Y., Aruga, Y., 1982. Variation of chlorophyll a concentration and photosynthetic activity of phytoplankton in Tokyo Bay. La Mer 20, 75–92. Sobrino, J.A., Jiménez-Muñoz, J.C., Paolini, L., 2004. Land surface temperature retrieval from LANDSAT TM 5. Remote Sens.Environ. 90 (4), 434–440. Sravanthi, N., Ramana, I.V., Yunus_Ali, P., Ashraf, M., Ali, M.M., Narayana, A.C., 2013. An algorithm for estimating suspended sediment concentrations in the coastal waters of India using remotely sensed reflectance and its application to coastal environments. International Journal of Environmental Research 7 (4), 841–850.

25

Stumpf, R., Culver, M., Tester, P., Tomlinson, M., Kirkpatrick, G., Pederson, B., Truby, E., Ransibrahmanakul, V., Soracco, M., 2003. Monitoring Karenia brevis blooms in the Gulf of Mexico using satellite ocean color imagery and other data. Harmful Algae 2 (2), 147–160. Suzumura, M., Kokubun, H., Arata, N., 2004. Distribution and characteristics of suspended particulate matter in a heavily eutrophic estuary, Tokyo Bay, Japan. Mar. Pollut. Bull. 49 (5), 496–503. Svendsen, E., Skogen, M., Albretsen, J., Søiland, H., Hackett, B., Pettersson, L., Johannessen, J., Lars Petter Røed, L., 2004. Capacity to monitor and model harmful algae blooms, and feasibility to produce assessment products for eutrophication. Report on Tasks, 5. TBEIC, 2015. 〈http://www.tbeic.go.jp/〉. (last accessed 01.09.15). Tebbs, E., Remedios, J., Harper, D., 2013. Remote sensing of chlorophyll-a as a measure of cyanobacterial biomass in Lake Bogoria, a hypertrophic, saline–alkaline, flamingo lake, using Landsat ETM. Remote Sens. Environ. 135, 92–106. Terada, T., Seki, H., Ichimura, S., 1974. An aerial distribution of microbial biomass in Tokyo Bat at Summer stagnation period. La Mer 12, 192–196. TMGBE, 2015. Tokyo bay red tide occurrence 〈http://www.kankyo.metro. tokyo.jp/water/tokyo_bay/red_tide/index.html〉 (last accessed 01.09.15). Tomlinson, M.C., Stumpf, R.P., Ransibrahmanakul, V., Truby, E.W., Kirkpatrick, G.J., Pederson, B.A., Vargo, G.A., Heil, C.A., 2004. Evaluation of the use of SeaWiFS imagery for detecting Karenia brevis harmful algal blooms in the eastern Gulf of Mexico. Remote Sens. Environ. 91 (3), 293–303. Tsuji, T., Seki, H., Hattori, A., 1974. Results of red tide formation in Tokyo Bay. J. Water Pollut. Control Fed., 165–172. USGS, 2015a. Landsat 8 (L8) Data User Handbook v1.0. 〈https://landsat. usgs.gov/documents/Landsat8DataUsersHandbook.pdf〉 (last accessed 08.09.15). USGS, 2015b. Provisional Landsat 8 Surface Reflectance Product Guide. 〈http://landsat.usgs.gov/documents/provisional_l8sr_product_guide. pdf〉 (last accessed 01.09.15). USGS Earth Explorer 〈http://earthexplorer.usgs.gov〉 (last accessed 01.09.15). Walsh, J.J., Steidinger, K.A., 2001. Saharan dust and Florida red tides: the cyanophyte connection. J. Geophys. Res.: Oceans (1978–2012) 106 (C6), 11597–11612. Zhao, J., Ghedira, H., 2014. Monitoring red tide with satellite imagery and numerical models: a case study in the Arabian Gulf. Mar. Pollut. Bull. 79 (1), 305–313.