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.