Spatial and temporal distributions of intense cyanobacteria blooms in Taihu
Lake, China between 2000 and 2008 from MODIS observations
Abstract
A novel approach was used with MODIS data between 2000 and 2008 to document
and study the spatial/temporal patterns of intense cyanobacteria blooms in Taihu Lake, a
medium-sized (~2300 km2) eutrophic lake in the eastern China that receives nutrient and
water inputs from the Yangtze River as well as point and non-point sources from local
industry and agriculture. The approach first derived a Floating Algae Index (FAI), based
on the medium-resolution (250- and 500-m) MODIS reflectance data at 645-, 859-, and
1240-nm after correction of the Ozone/gaseous absorption and Rayleigh scattering effects,
and then objectively determined the FAI threshold value (FAI = -0.004) to separate the
bloom and non-bloom waters. Compared with other traditional methods including NDVI
(normalized difference vegetation index) and EVI (enhance vegetation index), the
approach is less sensitive to changes in environmental conditions such as aerosol type
and thickness, sun glint, and solar/viewing geometry, and therefore enabled a consistent
time-series to be derived. The approach was validated by concurrent higher-resolution
(30-m) Landsat-7/ETM+ observations and also by the long-term patterns observed in
known areas in the lake.
The bloom distribution patterns, their annual occurrence frequency, timing, and
duration were documented and studied for each pre-defined lake segment and also for the
lake as a whole. Overall, bloom patterns and statistics were different between 2000-2004
and 2006-2008, with 2005 as the transition year. Using 25% area coverage as a gauge for
significance, significant bloom event never occurred between 2000 and 2004 or occurred
only several times for the entire lake (excluding East Bay) as well as for the NW Lake,
SW Lake, and Central Lake. Between 2006 and 2008, for most of the lake segments and
for the lake as a whole, the annual frequency of significant blooms increased from the
2000-2004 period, with earlier timing and longer duration. Occasionally the bloom
covered more than half of the lake during these years. The year of 2007 showed
exceptional patterns due to bloom-favorable wind, water level, and possibly temperature.
The bloom created enormous social-economical burden to local residents and government,
suggesting that despite the significant effort and resource put in the water-quality
management from the past decade, it requires a long-term, continuous, and persistent
management plan to bring back the lake’s water quality to the 2000-2004 level, not to
mention to the pre-industry boom era in the 70s. Nevertheless, the novel, multi-year
series of consistent MODIS data products provide a baseline to monitor the lake’s bloom
condition, one of the critical water quality indicators, on a weekly basis as well as to
evaluate its long-term trend in the future.
Keywords: cyanobacteria bloom, blue-green algae, floating algae, water quality, Taihu
Lake, remote sensing, MODIS, floating algae index (FAI), NDVI, EVI
Introduction
Kun – you can focus your writing on the first 3 below.
1. About Taihu Lake: location, size, average depth, weather (dry-season and
wet-season, precipitation in centimeters); chlorophyll-a concentration range;
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CDOM absorption range; TSM range, Temperature range. Other water qulity
parameters (e.g., COD, oxygen)? Long-term trend in these parameters?
2. Water quality event: 2007 event is an extreme – describe briefly (cite the Shi
and Wang EOS paper or other reference). Other historical events?
3. Current water quality monitoring: In situ methods (what’s available? How
many stations? What do they measure? How often? Where are the data? Any
reference?); Remote sensing methods (who? What? Most focused on the
concentrations of chl, CDOM, TSM, water clarity/turbidity. Few studied
floating algae using NDVI – describe how they did it)
Introduction
Coastal eutrophication is a serious global problem, especially in developing countries
where rapid-growing agriculture and industries provide excessive nutrients and other
pollutants through point and non-point sources to lakes, estuaries, and other coastal
waters. As a result, coastal resources are under perceptible stress, with significant
degradation in water quality, biodiversity, and fish abundance. For example, since 1998,
both the number and size of toxic algae blooms as well as the toxic species in Chinese
coastal waters in the East China Sea, Yellow Sea, and Bohai Sea have increased
significantly (Zhou and Zhu, 2006). Likewise, Brand and Compton (2007) found
increasing trend in Karenia brevis bloom frequency and intensity from the 50s to the
2000s on the west Florida shelf.
The problems present difficult challenges for both scientists and coastal managers
who often found that traditional techniques are inadequate to cover the range of space and
time scales involved in assessing environmental conditions, documenting the long-term
trends in water quality, and in studying anthropogenic and climate influence as well as
the various processes taking place in coastal waters, estuaries, and lakes. For example,
there is still ongoing debate on whether the observed trend in Brand and Compton (2007)
was biased by an observer effect.
Satellite remote sensing provides rapid, synoptic, and repeated information on water
state variables (physical and biogeochemical) to avoid the undersampling problems in
traditional techniques. Indeed, over the past three decades, there have been significant
advances in technology and algorithm development to use satellite ocean color
measurements to study coastal ocean water quality (e.g., Dekker, Lee, Hu, Kuster, etc.).
However, most of the work has been focused on turbidity and water clarity. Due to the
inherent problems in atmospheric correction and bio-optical inversion in estuarine waters
where sediments and other non-living constituents (e.g., tripton, colored dissolved
organic matter or CDOM, and shallow bottom) often play a dominant role in affecting the
spectral reflectance of the water, to date there has been no published work in establishing
a long-term, reliable record of phytoplankton blooms in any estuaries based on satellite
data.
In this paper, using a novel method and 9-year operational MODIS (MODrate
resolution Imaging Spectroradiometer) data over a heavily polluted and eutrophic lake in
eastern China, Taihu Lake, we developed a long-term record of intense cyanobateria
blooms in the lake. Our objectives are three fold: 1) to provide reliable statistics of
intense blooms to help understand the changing water-quality state of this socio-
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economically important lake. The results will serve as baseline data for future evaluation
of the lake’s water quality. Indeed, despite coordinated effort in monitoring and
management in the past decade, such a record is still lacking; 2) to better understand the
2007 bloom event that caused significant public burden and economic loss; 3) to
demonstrate the global applicability of this approach in similar estuaries or lakes with
heavy pollution.
We will first briefly introduce our study area and review the existing techniques for
water quality assessment, followed by description and validation of our approach to
quantify intense cyanobateria blooms. Then, the detailed statistics of the bloom patterns
between 2000 and 2008 are presented and discussed, with particular focus on the 2007
bloom event. Finally, we discuss the implications of these findings to the long-term water
quality assessment in Taihu Lake and in other similar water bodies.
Study Area
Taihu Lake (30°5'-32°8'N and 119°8'-121°55'E) is the third largest freshwater lake in
eastern China, with surface area of 2,338 km2 and average water depth of 1.9 m (Fig. 1).
It is located in semi-tropical monsoon climate, with high wind during winter but more
precipitation during summer (Fig. 2). Average annual precipitation is about 1,100 to
1,150 mm, with average water temperature of 15-17oC (Baosuo Jia et al., 1999). The lake
is traditionally divided in seven major segments including four embayments: Zhushan
Bay, Meiliang Bay, Gong Bay, and East Bay (MAO Jingqiao, 2008) (Figure 1).
The lake serves as the main freshwater source of 3 million [how many exactly?]
people in the nearby Wuxi City, and it also provides irrigation to local agriculture. From
the 1980’s the lake becomes highly eutrophic (Ling Ding et al., 2007). At the end of 1998,
local government enforced strict management practices (the so called “ZERO Action”)
that set up nutrient standards for all wastewater discharged into the lake. However, it has
been shown that the “ZERO Action” had little effect on reducing the eutrophic state
(Huang Wenyu et al., 2002), possibly due to recycled nutrients from the benthic
sediments. Between 1991 and 2007, total phosphorus, total phosphorus and suspended
solids from several discrete monitoring stations showed increasing trend with decreasing
water clarity, especially during 2001 to 2007 (Zhu Guangwei, 2008). Concentrations of
chlorophyll-a (Chl) and total suspended matter (TSM) show clear, opposite seasonal
patterns (Fig. 2).
Whthin the lake, the most eutrophic area is Meiliang Bay (Ronghua Ma, Jinfang Dai,
2005) which is the freshwater source of Wuxi City. During May - June 2007, extensive
blooms of the blue-green algae (what’s its species name?) occurred in Meiliang Bay,
resulting in rotten tap water in the city with nearly 1 million people affected (Duan
Hongtao et al., 2008; Wang and Shi et al., 2008). The event has been reported extensively
by local news media and received wide national and international attention. Preliminary
studies (who?) suggested that the event resulted from an earlier-than-usual algae bloom
due to warm winter, low water level, and favorable wind. However, there has been no
report on exactly how/where the bloom initiated and evolved.
Clearly, a thorough understanding of the algae blooms together with a comprehensive
monitoring and management plan are required to improve the lake’s water quality. Indeed,
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a monitoring network has already been implemented, where water samples at pre-defined,
fixed stations are collected and analyzed periodically (Ref?). Routine ship surveys have
also been used between 2002 and the present (Ref?). These provided water quality data
for nitrogen, phosphorous, dissolved oxygen, Chl, TSM, temperature, and other
pollutants. Of particular interest are the Chl and TSM measurements because they
represent the water’s biological state and physical state (water turbidity), respectively.
Although in theory both Chl and TSM can be derived from remote sensing
measurements, in practice this has been difficult for estuaries and lakes due to errors in
atmospheric correction and optical complexity in these waters. Several regional
algorithms have been proposed from limited field data (Ronghua Ma, Jinfang Dai,
2005a&b; Zhu Lingya et al., 2006), yet their applicability in deriving long-term timeseries is unclear.
Because in the intense blooms the blue-green algae often form thick mats on the
surface that appear as land vegetation, simple methods using NDVI (Normalized
Difference Vegetation Index) and EVI (Enhanced Vegetation Index) have also been
proposed from limited field and satellite data (Xu Jingping et al., 2008; Chen Yun, Dai
Jinfang, 2008). However, Hu (submitted) showed that both NDVI and EVI are sensitive
to aerosols (type and thickness), sun glint, and solar-viewing geometry and it is therefore
difficult to derive a consistent time-series. This is different from the algae bloom event
between May and July near Qingdao (China) where the algae formed distinguishable thin
slicks which can be clearly recognized and delineated from the MODIS NDVI imagery
(Hu and He, 2008). Ma et al. (2008) applied a band-ratio method (859 versus 555 nm) to
MODIS 250-m resolution data to separate bloom and non-bloom waters, and then derived
a long-term distribution pattern of the blooms in Taihu Lake. Because of lack of
atmospheric correction, similar limitation as faced by NDVI and EVI applies. Clearly, an
improved method is required to establish a consistent long-term record of the bloom
distributions.
Data and Method
This work relied mainly on the operational MODIS data, but less-frequent Landsat7/ETM+ data at 30-m resolution were also used to validate the MODIS observations.
MODIS Data General Processing
MODIS Level-0 (raw digital counts) data from both Terra and Aqua satellites were
obtained from the U.S. NASA Goddard Flight Space Center (GSFC). Although Terra was
launched in 1999, global Level-0 data started from February 2000. Because of the large
data volume (> 9000 data granules covered Taihu Lake), the quick-look browse images
were first visually examined, and those with minimal cloud cover were downloaded and
processed. Between February 2000 and December 2008, about 630 near-cloudfree Level0 granules were downloaded.
The Level-0 data were converted to calibrated radiance data using the software
package SeaDAS (version 5.1). Then, gaseous absorption and Rayleigh scattering were
corrected using computer software provided by the MODIS Rapid Response Team, based
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mainly on the radiative transfer calculations from 6S (Vermote et al., 1997). The resulting
reflectance data, Rrc() where  is the wavelength of the MODIS bands (469, 555, 645,
859, 1240, 1640, 2430 nm), were geo-referenced to a cylindrical equidistance
(rectangular) projection using computer programs developed in house. The geo-reference
errors were less than 0.5 pixel.
Three types of imagery were generated from the geo-referenced Rrc(). The first was
the Red-Green-Blue “true-color” composite, using 645, 555, and 469-nm as the Red,
Green, and Blue channels, respectively. The 500-m resolution data at 555 and 469-nm
were resampled to 250-m resolution (to match the resolution at 645-nm) using a
“sharpening” scheme similar to that used for Landsat data. The second was the simple
NDVI image, derived as NDVI = [Rrc(859) – Rrc(645)]/[Rrc(859) + Rrc(645)]. The purpose
was to provide a simple image set for quick examination, although it is known that NDVI
suffers from aerosol and sun glint effects.
The last image type was a Floating Algae Index (FAI), introduced by Hu (submitted).
For MODIS data, FAI is defined as
FAI =
Using model simulations and MODIS measurements, Hu (submitted) showed that
compared with NDVI and EVI, FAI was much less sensitive to changes in environmental
conditions (aerosol type and thickness, sun glint, solar/viewing geometry). Although the
index was designed to search for floating algae in the open oceans, these characteristics
suggest that FAI may be used to derive a long-term bloom pattern and trend in Taihu
Lake.
An example of the three image types is shown in Fig. 3, where images from three
consecutive days (5/19/2008 – 5/21/2008) are presented. The RGB images suggest that
the atmospheric conditions on 5/19/2008 and 5/21/2008 were similar, but hazy
atmosphere and sun glint were present on 5/20/2008. Using the Cox and Munk (1954)
surface roughness model and NCEP wind data, sun glint reflectance Lg (Wang and Bailey,
2001) was estimated as 1.710-5, 0.05, and 0.0 sr-1 for the three days, respectively. Even
under this extreme condition on 5/20/2008 (Lg = 0.05 sr-1 and Rrc(1640) = 0.139), floating
algae can still be clearly delineated from other waters (see below for the delineating
method) and the results appear consistent with the two adjacent days. Note that Lg = 0.01
sr-1 is regarded as significant in ocean color data (too large to be correctable) and
Rrc(1640) > 0.0275 would be recognized as cloud by a relaxed cloudmasking method
(Wang and Shi, 2006). Clearly, the FAI method is robust for virtually all conditions in
this region (thick aerosol, frequent sun glint during summer). In contrast, because the
higher sensitivity of NDVI to aerosol and sun glint influence (Fig. 3 NDVI image on
5/20/2008), it is practically more difficult to use NDVI to derive a consistent time series
under most circumstances.
Landmask
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Cloudmask
FAI threshold
The most critical task in delineating floating algae from other waters was to
determine the threshold value in the FAI imagery. One could use trial-and-error together
with visual analysis, but this was subjective and therefore arbitrary. Instead, we used FAI
gradients and statistics to determine this critical value.
For each FAI image (with land and cloud masked), a gradient image was generated. A
pixel’s gradient was defined as the FAI difference from the adjacent pixels in a 3x3
window. Histogram of the gradient image was generated and the mode determined. It was
found that the pixels associated with the mode could delineate floating algae very well.
This is understandable because at the algae and non-algae boundary there should be a
sharp change (large gradient) in the FAI values. The mean FAI value of all pixels
associated with the mode was used to represent the threshold value (FAIthresh) to delineate
floating algae.
The method was applied to the entire image series and it worked well for most of the
images (from visual examination), especially when extensive floating algae were found.
However, in images where floating algae patches are small, due to fewer pixels pooled in
the histogram the method to use the mode no longer makes sense. Therefore, instead of
using a different FAI threshold value for each image, all FAI threshold values (after
excluding those with none or small floating algae patches) were pooled together to
compute the histogram as well as the mean and standard deviation (Fig. 4). Then, a
universal FAI threshold was determined as the mean minus 2 times of the standard
deviation, which was approximately -0.004. This value was chosen as a time-independent
FAI threshold to delineate floating algae from other waters. Indeed, a sensitivity analysis
(not shown here) suggested that using FAIthresh = -0.004 and FAIthresh = 0.0 would result
in nearly identical statistics and spatial/temporal patterns, except that the former typically
led to 5-10% higher floating algae area coverage.
The FAI threshold (-0.004) was validated using Landsat-7/ETM+ data. The 30-m
resolution data were downloaded from the United States Geological Survey and
processed in a similar fashion as with MODIS. Because of the higher resolution, the
floating algae in Landsat imagery show spatial texture that can be clearly visualized (Fig.
5), therefore can be manually outlined and used to validate the MODIS results. Fig. 5
shows an example of such comparison. The manually-derived floating algae outlines (red
lines in Fig. 5b) in the Landsat image and the objectively-derived floating algae outlines
(blue lines in Fig. 5b) in the concurrent MODIS image are nearly identical, suggesting the
validity of the FAI threshold method. Although in certain areas when the algae may be
submersed in water due to wind and current so they only appear in Landsat RGB image
but not in MODIS FAI image (once the algae are below the water surface their signal in
the 859-nm band decreases rapidly), these areas and their associated errors are small.
Indeed, further comparison using other Landsat/MODIS image pairs when both were
cloud free showed very similar results between the two different measurements, with area
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coverage to within ±10% (Table 1). Therefore, a FAI threshold of -0.004 was used for the
entire MODIS image series to derive the floating algae distributions and statistics.
MODIS Sea Surface Temperature (SST)
Results
Discussion and Conclusion
Algae as an indicator responds to a change in environmental conditions very quickly
(Coesel et al., 1978), and dynamic growth of algae is influenced by the inner
physiological characteristics as well as the exterior stimulating factors, including light,
temperature and nutrients (Ling Ding et al., 2007).
Acknowledgement
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Tables and Figures
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