extraction the time and space distribution information of algae
bloom. 2) Based on extracted information and index, obtaining
the dynamic feature of time and space in Taihu Lake from 2009
to 2011. 3) Analyzes and discusses variation of cyanobacterial
bloom in the Taihu Lake during this time.
Ders th
a. The water body of Lake Taihu is divided into nine water
regions: Xukou Bay, Meiliang Bay, Zhushan Bay, Gonghu Bay,
Central Lake, Western Coast, Southern Coast, Jianhu and
Dongjiaoju, East Taihu Bay (Jin Y et al, 2009; Liu Jet al, 2011;
Zhang Y., et al , 2011; Duan, H, et al, 2009).
b. The satellite imagery: the blooming found by MODIS
imagery in Lake Taihu on August 17th, 2009.
Figure 1 Location, morphology and division of Taihu Lake
2. DATA AND METHOD
2. Data Acquisition and Pre-processing
MODIS sensors are carried by TERRA and AQUA satellite
in EOS plan of the United States. It cover the visible to the near
infrared (0.4- 1.4 um) spectrum With 36 spectrum channel and
its scanning width is 2330 km, whose spatial resolution is 250m,
500m and 1000 m and the temporal resolution is twice a day.
MODIS image data is often used in an eutrophication
monitoring of lakes (Kong W et al, 2009). Excluding the
weather reason such as clouds and rain, there are 374 MODIS
images over Lake Taihu that used to extract the spatiotemporal
information of algal blooms (Table 1). The data in the period of
2009-2011 were downloaded from the NASA EOS Data
Gateway (EDG).
Year
2009 2010 2011
International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XXXIX-B7, 2012
XXII ISPRS Congress, 25 August — 01 September 2012, Melbourne, Australia
Effective monitoring day 128 150 96
Algal bloom days by
97 74 38
Monitoring
Cloudless days 103 150 65
Note: Effective monitoring day means the days of completing activitie
s of monitoring indicator normally. Cloudless days are the days witho
ut cloud cover within the whole Taihu Lake region in a year.
Table 1 The total days of the status of monitoring algae bloom
using MODIS image in 2009, 2010 and 2011, Taihu Lake
In order to obtain the algae bloom space distribution data of
Taihu Lake in effective monitoring days using MODIS data, the
cutting operation, geometric correction, and radiation
calibration should be executed firstly. Geometric correction can
be doing using longitude and latitude data in HDF original file
and building a geographic lookup table to transform the image.
Radiation calibration can be executing using gain and offset
coefficient value of each band in HDF file realizing the
transform from DN to radiance value. In order to get the truth
value of water surface radiation and reflectance, it is necessary
to do atmosphere correction, in the process of correcting, the
FLAASH model (Kaufman, Y et al, 1997) in ENVI being
adopted. In that the eastern of Taihu Lake (including Jianhu and
Dongjiaoju, East Taihu Bay and Xukou Bay), in which there
are many growth of large phytoplankton belong to the type of
palustrine and shallow Lake. In the process of recognition using
remote sensing image, it is often to take aquatic vegetation for
cyanobacteria bloom, so before there is no better identification
mode, the eastern of Taihu lake region will be as aquatic
vegetation area automatically in this paper (Jin Y et al, 2009;
Ma R et al, 2010). NDVI (Jensen, J. R., 1986) is useful to
extraction space distribution information in Effective
monitoring days (Hu C et al, 2000). Usually, handling of NDVI
can partly eliminate the influence of the height of the sun Angle,
satellite viewing angles, and the terrain to extract algae bloom
information (Hu C, 2009).
2.2 The Time and Space Distribution Index
The algae bloom frequency index (AFI) is defined as the
number of the algae bloom days against the number of effective
monitoring days marked as T in a certain water area at certain
time. Using MODIS data in algae bloom monitoring, the size of
a certain water area is the 250m*250m, a size of a pixel, and a
certain time usually is 365day in a year. The mathematical
expression is as the follow:
AFI -t/ T*10096 (1)
AFI can reflect the affected degree in some area by algae bloom
at certain time (Jin Y et al, 2009; Ma R er al, 2010). Actually, a
certain time in the definition of AFI is identified as 365day in a
year so as to make it as the index of annual degree of algae
bloom in certain water region. This kind of AFI is called as the
annual algae bloom frequency index (AAFI). In order to
measure the intermonth variation, a certain time is often set to
the days of a month in a year and certain water region is
defined as the whole Taihu Lake or a subregion. This kind of
AFI is called as the month algae bloom frequency index
(MAFD.
