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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
SEASONAL DIFFERENCES IN SPATIAL SCALES OF CHLOROPHYLL-A
CONCENTRATION IN LAKE TAIHU, CHINA
Ying Bao^, Qingjiu Tian * *, Shaojie Sun*, Hongwei Wei“, Jia Tian
8 8) ]
? International Institute for Earth System Science, Nanjing University, Nanjing 210093, China-
qiheye100@163.com,tiangj@nju.edu.cn,sunshaojie87@126.com,piaoxue.6416931@163.com
^ School of Electronic Science and Engineering, Nanjing University, Nanjing 210093, China- tian-jia08@163.com
Commission VII, WG VII/5
KEY WORDS: Chlorophyll-a concentration, Scale Heterogeneity, Scale Error, Different Seasons
ABSTRACT:
Spatial distribution of chlorophyll-a (chla) concentration in Lake Taihu is non-uniform and seasonal variability. Chla concentration
retrieval algorithms were separately established using measured data and remote sensing images (HJ-1 CCD and MODIS data) in
October 2010, March 2011, and September 2011. Then parameters of semi- variance were calculated on the scale of 30m, 250m and
500m for analyzing spatial heterogeneity in different seasons. Finally, based on the definitions of Lumped chla (chlaL) and
Distributed chla (chlaD), seasonal model of chla concentration scale error was built. The results indicated that: spatial distribution of
chla concentration in spring was more uniform. In summer and autumn, chla concentration in the north of the lake such as Meiliang
Bay and Zhushan Bay was higher than that in the south of Lake Taihu. Chla concentration on different scales showed the similar
structure in the same season, while it had different structure in different seasons. And inversion chla concentration from MODIS
500m had a greater scale error. The spatial scale error changed with seasons. It was higher in summer and autumn than that in spring.
The maximum relative error can achieve 2396.
1. INTRODUCTION
Chlorophyll-a (chla) plays a significant role in water ecosystem.
It is a basic indicator of lake eutrophication (Zhou et al., 2009).
The change of its spatial distribution and concentration can
influence the lake ecosystem. In recent decades, water quality
remote sensing has become an effective way to monitor it.
However, spatial heterogeneity could cause scale effect in the
retrieval of chla concentration from multi-resolution remote
sensing images (Bao et al., 2011). It brings scale error (Chen et
al, 2010). And the error affects the retrieval accuracy and
varies with seasons. Therefore, studying seasonal differences in
spatial scales of chla concentration is useful for improvement of
the retrieval accuracy.
Spatial scale effect and uncertainty based on remote sensing
images had been studied domestic and abroad. Moran
coefficient, Geary ration, coefficient of variation and
variograms were the most commonly used methods for
analyzing spatial heterogeneity (Zhang, 2008). Researches
usually provided variogram function for studying spatial
heterogeneity and spatial effect of water quality parameters.
They found that spatial distribution of chla concentration exist
structure (Xia et al., 2011; Liu et al., 2002). In addition, new
methods such as hydrodynamic model, biological model were
introduced to solve practical problem (Chen et al., 2010;
Fragoso et al., 2008). Moreover, spatial scale uncertainty is also
a hotspot in current studying (Zhang, 2008). Radiative transfer
model, regressive model and fractal theory are popular methods
for studying spatial errors and spatial scaling (Chen et al., 2006).
But most methods for spatial uncertainties were based on
vegetation. The work for water quality parameters has not been
extensively researched. As there is no sensor for inland water
* Correspond author: Qingjiu Tian, Professor in Nanjing University, China. Main research direction is Hyperspectral remote sensing
quality remote sensing, land satellites and ocean colour satellite
were used to estimate chla concentration (Zhou et al., 2009).
Both of the data souses and spatial heterogeneity of chla
concentration can affect the retrieval accuracy.
In this paper, Lake Taihu in the east of China was selected as
the study area. Based on the chla concentration retrieval
algorithm and the results of spatial effects, a model of spatial
scale error was established for MODIS 500m. Then the paper
analyzed seasonal results which were calculated in different
spatial scales.
2. STUDY AREA AND DATA PREPROCESS
2.4 Study Area
Lake Taihu is one of the five largest freshwater lakes in China.
The lake, situated in the southeast of the country (30°55'40"-
31?32'58"N, 30?55'40"-31?32'58"E), is a typical inland shallow
lake with a water area of 2388km? (Tang et al., 2007; Zhang et
al. 2011). Taihu Basin is located in Shanghai, Jiangsu and
Zhejiang Province (see Figure 1.). It is one of the most
developed regions in China. In recent years, water pollution of
Lake Taihu is more and more serious because of the rapid
development of the economy (Feng et al, 2007). And the
pollution (especially eutrophication) has been attracted great
attention.
In the East and Eastern Lakeshore of the lake, water quality is
clear and remains stable in good condition. No cyanobacterial
bloom has been found so far in these areas (Ma et al., 2006).
But the chla concentration content of blue-green algae in the
other part of Lake Taihu is high and variable. The spatial
