monitoring long-term ocean health using remote sensing… · monitoring long-term ocean health...

Monitoring Long-term Ocean Health Using Remote Sensing: A Case Study of the Bay of Bengal

Lim Jin Yia, Md Latifur Rahman Sarker*ab, Lei Zhangc, Eko Siswantod, Ahmad Mubina, Saadah

Sabarudina

aDepartment of Geoinformation, Universiti Teknologi Malaysia, Malaysia; bDepartment of Geography and Environmental Studies, University Of Rajshahi, Bangladesh; cDepartment of Land

Surveying and Geo-Informatics, Polytechnic University, Hong Kong; dEnvironmental Biogeochemical Cycle Research Program (EBRCP), Research Institute For Global Change (RIGC),

Japan Agency For Marine-Earth Science And Technology (JAMSTEC), Kanagawa, Japan.

ABSTRACT

Oceans play a significant role in the global carbon cycle and climate change, and the most importantly it is a reservoir for plenty of protein supply, and at the center of many economic activities. Ocean health is important and can be monitored by observing different parameters, but the main element is the phytoplankton concentration (chlorophyll–a concentration) because it is the indicator of ocean productivity. Many methods can be used to estimate chlorophyll–a (Chl-a) concentration, among them, remote sensing technique is one of the most suitable methods for monitoring the ocean health locally, regionally and globally with very high temporal resolution.

In this research, long term ocean health monitoring was carried out at the Bay of Bengal considering three facts i.e. i) very dynamic local weather (monsoon), ii) large number of population in the vicinity of the Bay of Bengal, and iii) the frequent natural calamities (cyclone and flooding) in and around the Bay of Bengal. Data (ten years: from 2001 to 2010) from SeaWiFS and MODIS were used. Monthly Chl–a concentration was estimated from the SeaWiFS data using OC4 algorithm, and the monthly sea surface temperature was obtained from the MODIS sea surface temperature (SST) data. Information about cyclones and floods were obtained from the necessary sources and in-situ Chl–a data was collected from the published research papers for the validation of Chl-a from the OC4 algorithm. Systematic random sampling was used to select 70 locations all over the Bay of Bengal for extracting data from the monthly Chl-a and SST maps. Finally the relationships between different aspects i.e. i) Chl-a and SST, ii) Chl-a and monsoon, iii) Chl-a and cyclones, and iv) Chl-a and floods were investigated monthly, yearly and for long term (i.e 10 years). Results indicate that SST, monsoon, cyclone, and flooding can affect Chl-a concentration but the effect of monsoon, cyclone, and flooding is temporal, and normally reduces over time. However, the effect of SST on Chl-a concentration can't be minimized very quickly although the change of temperature over this period is not very large.

Keywords: Ocean health, chlorophyll-a concentration, sea surface temperature, season, natural disaster

1. INTRODUCTION The ocean plays an important role in many of the Earth's systems including climate and weather (NOAA, 2012). Phytoplankton, one of the microscopic organisms that inhabit the upper layer of ocean and it contains of chlorophyll which absorb sunlight in the process of photosynthesis. It plays an important role in the global carbon cycle and climate change (Chisholm, 2000). It causes climate change by changing the SST through the absorption of solar radiation for photosynthesis (Marzeion et al., 2005). Therefore, changes in phytoplankton biomass will bring momentous effect on ecosystem structure. There are several factors that can affect the Chl-a concentration, including variability of SST, season and occurrence of natural disaster. Hood et al., 1990, carried out a study at coast of Northern California and found that there is an inverse relationship between the SST and chlorophyll concentration. From previous studies (Wiggert et al., 2005; 2006), it is known that phytoplankton bloom activity is the result from the semiannual wind reversals associated with the monsoon system. Besides that, the occurrence of cyclone can induce the phytoplankton blooms by

* [email protected]; phone +60 1115158825

Remote Sensing of the Ocean, Sea Ice, Coastal Waters, and Large Water Regions 2013, edited by Charles R. Bostater, Jr., Stelios P. Mertikas, Xavier Neyt, Proc. of SPIE Vol. 8888,

88880K · © 2013 SPIE · CCC code: 0277-786X/13/$18 · doi: 10.1117/12.2029032

Proc. of SPIE Vol. 8888 88880K-1

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4.3 Correlation between chlorophyll-a concentration and sea surface temperature in the Bay of Bengal

This study depicted that there was relationship between Chl-a concentration and SST. However the relationship between Chl-a concentration and SST was not constant and depends on the location of the ocean. Generally, coastal region showed a positive relationship for SST and Chl-a concentration (Table 1), but some coastal region gave the relationship in the opposite way (Table 1). For example, Region A and B which were categorized as coastal regions were having a positive correlation for most of the past ten years, but region H and F were having a negative correlation for most of the ten years. Whereas for offshore areas, the relationship between Chl-a concentration and SST showed a good negative correlation (Table 1) and the highest negative correlation value was -0.88 (Table 1). Regions that are located in offshore areas had a negative correlation almost throughout the 10 years time. Positive correlation value was found in 2008 and 2009, though the positive correlation was very weak (≈0.1-0.4). Besides, one of the regions (region I) which were located around Andaman Island, the correlation between SST and Chl-a concentration was not so obvious. However, all regions in the BOB did not have negative or positive correlation value for the 10 years (Table 1).

Table 1. The correlation value between SST and Chl-a concentration from year 2001 to 2010 in BOB.

Region Year

A B C D E F G H I J

2001 0.07 0.057 -0.88 -0.37 -0.77 -0.71 -0.36 -0.46 -0.45 -0.47 2002 0.22 0.121 0.71 -0.25 -0.73 -0.40 -0.44 -0.35 -0.60 -0.56 2003 -0.0096 -0.142 0.69 -0.68 -0.31 -0.36 -0.83 -0.22 -0.88 -0.64 2004 0.22 0.095 -0.69 -0.23 -0.70 -0.53 -0.36 -0.16 0.15 -0.64 2005 0.69 0.346 -0.64 -0.071 -0.61 -0.71 -0.34 0.031 -0.029 -0.66 2006 -0.31 0.282 -0.69 -0.25 -0.82 -0.73 -0.13 -0.83 -0.10 -0.56 2007 0.32 0.175 -0.53 -0.037 -0.33 -0.70 -0.65 -0.024 0.11 -0.51 2008 0.65 0.362 0.19 0.43 0.33 0.30 0.27 0.20 0.00056 0.23 2009 -0.021 0.850 -0.50 0.14 -0.62 0.26 0.22 -0.51 0.066 -0.049 2010 0.274 0.549 -0.63 -0.56 -0.43 -0.10 -0.78 -0.03 0.10 -0.67

The probable reasons for this negative relationship are: i) stable water column in the offshore ocean, and ii) increasing in SST can increase the stress of the organism (Iius, 2008). However, negative relationships between Chl-a concentration and SST is very common and this result is in agreement with other researchers (Kamykowski, 1987; Chaturvedi, 2011) who found that Chl-a concentration has inverse relationship with SST and the relationship is known to be changed according to differ in location. Positive relationship was also observed some parts of the BOB especially in coastal area except for the two regions (region F and H). A positive correlation value indicates the variation of Chl-a concentration was not depending on SST variation. This can be explained by the fact that there were many factors that can affect the Chl-a concentration variation near the coastal area, including coastal upwelling, eutrophication, sediment and nutrient from river discharge. Though this result is not directly in agreement with other studies but in principle it agrees with previous findings of other study (Kumari and Babu, 2009) that the correlation between chlorophyll and SST is not always negative.

Moreover, alongside the positive relationships between Chl-a concentration and SST in the coastal region, negative correlation between Chl-a concentration and SST was also found in coastal region especially in two regions such as F and H regions. Although this trend is different from the normal relationship, previous study (Kumari and Babu, 2009) found region F is located in the area of BOB that undergoes two monsoon seasons. During the south west monsoon, this region was experienced surface wind forcing and caused the SST to reduce. Moreover, Ali et al., 2007, found that this wind forcing during south west monsoon can be attributed to cyclone-induced mixing, and this lead to the increase of Chl-a concentration for region F. Based on these findings, it showed that the finding of negative relationship between SST and Chl-a concentration for the region F agrees the previous studies. Region H had a negative correlation (high SST and low chl-a concentration) maybe due to the stratification. As mentioned before, region H is located at the river mouth where it receives a large amount of freshwater from the river. As a result, the waters are less saline compared to the other area (Gauns et al., 2005). Higher SST plus low salinity condition lead to strong stratification makes chl-a concentration to reduce.



20 °N

16 °N

12 °N

8 °N

November 2001 Chlorophyll -a concentration - Bay of Bengal

80 "E 84 "E 88 "E 92 "E 96 "E 100 °E

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February 2002 Chlorophyll -a concentration - Bay of Bengal65

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4.4 Effects of season and monsoon on chlorophyll-a concentration in the Bay of Bengal

The result of this study showed that the seasonal variation affect the Chl-a concentration. Based on the trend graph (Figure 2 to Figure 5) from 2001 to 2010 in the BOB, SST was lower during winter and the winter monsoon and higher during summer and the summer monsoon. During winter the SST started to decrease from November and started to increase from January. In contrast, Chl-a concentration during winter monsoon and winter was started to increase from November and started to decrease from January. This kind of variation was almost the same for the ten years time. The SST during winter was lower (27˚C) compared to the SST during summer (30˚C). However the SST was also varied according to location. During winter the SST for region that one near to coastal area was about 30˚C and during summer it was about 32˚C.

(a) (b)

(c) (d)

Figure 6. Chl-a concentration variation map in BOB during winter for November (a), December (b), January (c), and February (d) 2001.



20 °N

16 °N

12 °N

8 °N

May 2001 Chlorophyll -a concentration - Bay of Bengal

20 °N

16 °N

12 °N

8 °N

August 2001 Chlorophyll -a concentration - Bay of Bengal

10

80 °E 84 °E 88 °E 92 °E 96 °E

June 2001 Chlorophyll -a concentration - Bay of Bengal

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September 2001 Chlorophyll -a concentration - Bay of Bengal

20 °N

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12 °N

8 °N

20 °N

16 °N

12 °N

8 °N

July 2001 Chlorophyll -a concentration - Bay of Bengal

80 °E 84 °E 88 °E 92 °E 96 °E

10

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óU

Ú

0.1

0.01100°E

(a) (b)

(c) (d)

(e)

Figure 7. Chl-a concentration variation map in BOB during summer for May (a), August (b), June (c), September (d), and July (e) 2001.



According to the results from Figure 2 and 3, it showed that during the winter monsoon period (November to December), region E and F which were located in northwestern region of the BOB near Sri Lanka had an obvious Chl-a concentration variation during this monsoon period compared to other regions. It was observable that throughout the 10 years time, these two regions had the same trend pattern all the time i.e. Chl-a concentration increased between the months of November and February. This type of variation was also shown in the Chl-a map for the winter monsoon period (Figure 6). The Chl-a concentration increased as much as up to 0.6mg/m3 during the winter monsoon. The increase of Chl-a concentration around Sri Lanka area during winter monsoon was due to local Ekman pumping process which injected the nutrient to upper surface and triggered phytoplankton bloom (Vinayachandran and Mathew, 2003). While other regions remained less variant throughout the monsoon period.

From the results (Figure 2 and 3), it showed that during the summer monsoon period (May to September), regions that are located in the Northeastern part of the BOB underwent obvious Chl-a concentration variation compared to other regions. During the summer monsoon period, the peak Chl-a concentration in region A was achieved which was up to 9 mg/m3, while at region H it was about 7 mg/m3. The probable reason of this variation is that during summer monsoon the wind blow from southwestern part and passes through BOB, on the meanwhile, the wind pick up moisture from BOB and pour large amount of moisture in eastern Himalaya. The moisture was then discharge trough Ganges-Brahmaputra River to BOB (Wikipedia 2013). The large amount of discharge provided the nutrient for coastal area phytoplankton bloom during summer monsoon especially in region A and region H. During this monsoon period, Chl-a concentration in the middle part of the BOB also showed some variation (Figure 7). The variation of Chl-a concentration resulted in an increase of 0.59 mg/m3. This type of variation was found from 2001 to 2010.

In this research, the effect of natural disaster on the concentration of Chl-a was investigated but due to the limitation of data and time constraints this research only considered a few issues. It is observed from the result (Figure 2 and 3) that during inter monsoon period Chl-a concentration was increased in the region B (April, 2004). The probable reason for this increased of Chl-a concentration is the effect of Cyclone. Cyclone often occurs during inter monsoon period (from March to April and October to November) and Tropical cyclone creates anticlockwise wind in the Northern Hemisphere. Previous study (Taylor, 1989) indicated that cyclonic storm can induce phytoplankton bloom, and when cyclone happens, it churns up the water and transports the nutrient from the bottom to euphotic layer and finally trigger phytoplankton bloom (Vinayachandran, 2009). Besides that, according to NASA (2013), during late summer in year 2002, massive flood disaster happen in India and Bangladesh, and it was caused by monsoon rainfall. The massive flood discharged large amount of water in to BOB trough Ganges-Brahmaputra River. Based on the results (Figure 2) it showed that region A which is located at the river mouth of Ganges- Brahmaputra River has peak Chl-a concentration in September. These findings proved that natural disaster such as cyclone and flood has a potential to affect Chl-a concentration in BOB.

5. CONCLUSION AND RECOMMENDATION Satellite remote sensing provides good opportunity to observe long term ocean productivity or ocean color. Ocean color monitoring plays an important role to monitor the world climate change, ocean productivity and fisheries activity. Seasonal variation, monsoon season, SST and natural disaster are able to affect the ocean productivity. There is a relationship between SST and Chl-a concentration. But the relationship depends on geographical location of the BOB. However in generally, coastal areas have positive correlation and region at offshore area have negative correlation between SST and Chl-a concentration.

Seasonal and monsoon period also affected the Chl-a concentration in BOB. During summer and winter, Chl-a concentration in BOB had negative relationship in offshore area and coastal area. However, the monsoon season is knows to affect the Chl-a concentration in BOB and the occurrence of seasonal monsoon increased the Chl-a concentration in certain area. The Northeastern monsoon increased the Chl-a concentration near Sri Lanka area, while the Southeastern monsoon increased Chl-a concentration near the northeastern part of the BOB.

Beside monsoon, natural disaster also affected the Chl-a concentration in BOB. Occurrence of cyclone during inter monsoon period increased the Chl-a concentration in BOB because cyclone act as strong wind force that transport nutrient from bottom to upper layer and cooling the SST, and this process triggered the bloom of phytoplankton. Flooding also affected the Chl-a concentration in BOB due to the large amount of nutrients water discharge from land which induced phytoplankton bloom.



ACKNOWLEDGEMENTS

The authors would like to thanks Faculty of Geoinformation and Real Estate and Universiti Teknologi Malaysia (UTM) for support of this study. Special thank also goes to Ministry of Higher Education (MOHE) for providing grant (Q.J130000.2527.03H71) under the title “Land-use and Land-cover Change and Mapping of Ecosystem Based Carbon Storage Capacity in Wet Tropical Asian Bioregion”.

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