Figure 3. Comparison of chla concentration estimated from
different scales on September 4^. 2011 (summer). (a)Chla
concentration estimated from HJ-1 CCD (b) Chla concentration
estimated from MODIS 250m (c) Chla concentration estimated
from MODIS 500m
Legend
[273 sand
CU Fall Bewakary
wtp Lo
se. 20 CU AO as
26-40 Ad. oo HEE 149. pel
46.69 HEE 100.120 SEE ee
:
TT
Figure 4. Comparison of chla concentration estimated from
different scales on October 31%, 2010 (autumn). (a)Chla
concentration estimated from HJ-1 CCD (b) Chla concentration
estimated from MODIS 250m (c) Chla concentration estimated
from MODIS 500m
From the results above, the spatial distribution of chla
concentration had different trends in different seasons. Chla
concentration is regional and seasonal. Because the upstream
rivers of Lake Taihu in Jiangsu section are mainly in Meiliang
Bay, Zhushan Bay, and west coast, and in Zhejiang section are
mainly concentrated in the south coast of Lake Taihu. Water in
Meiliang Bay and Zhushan Bay is relatively static. By the wind
effect, cyanobacteria will accumulate in these areas (Xia et al.,
2011; Sha et al., 2009). In recent years, the TN, TP of central
lake region increased gradually to achieve the optimal growth
conditions for cyanobacteria. This region was more suitable for
the growth of cyanobacteria than the previous (Zhu, 2008).
Therefore, in summer when cyanobacteria blooms were grew
and in autumn when blooms were floating gathered, chla
concentration was high in these regions. In addition, because of
MODIS low spatial resolution, mixing phenomena of
cyanobacteria and water, early warning detection accuracy
cyanobacteria blooms was reducd.
4.2 Analysis of Spatial Heterogeneity
Method of field quadrats laid not only lack of sampling points
and time-consuming, but also difficult to control the sampling
time difference within a certain range (Xia et al., 2011). Thus,
this article selected sample points from the inversion chla
concentration image to study the spatial heterogeneity. 3km x
3km of quadrats were selected in Meiliang Bay and Central
Lake (measured data mainly in these two regions). The pixel
values in the quadrat were seen as sample points which were
normal distribution after logarithmic transformation (excluding
outliers) . Then semi-variance values were respectively fitted to
obtain parameters, such as Table 1., Table 2., Table 3.
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
Scale v0 yO+y a(m) = y/(y0+y)
30m | 134E-02 9.90E-02 2563 0.865
Mei 250m | 8.00E-04 6.80E-02 3995 0.988
500m | 1.20E-04 241E-02 5110 0.995
30m | 1.14E-01 4.68E-01 4328 0.756
e. 250m | 1.34E-03 7.10E-03 4026 0.811
500m | 2.90E-04 1.69E-02 4097 0.983
Table 1. Parameters of semi-variance in spring
Scale v0 yO+y a(m) y/(y0+y)
30m 1.58E-03 9.76E-03 1548 0.838
Mei 250m | 1.80E-04 1.20E-02 1230 0.985
500m | 3.30E-03 5.01E-02 4518 0.934
30m | 7.80E-03 3.30E-02 4387 0.764
Cor 250m | 4.40E-03 2.08E-01 2722 0.979
500m | 9.50E-03 5.00E-02 4705 0.810
Table 2. Parameters of semi-variance in summer
Scale v0 vO0+y a(m) y/(y0+y)
30m | 5.20E-03 022 3213 0976
Mei 250m | 2.80E-03 021. 3/2568 1 0987
500m | 1.00E-04 0.19 4728 0.999
30m | 3.30E-02 121 2495 0973
ce 250m | 8.00E-03 1285 1880. 0.994
500m | 1.00E-03 1.04 5110 0.999
Table 3. Parameters of semi-variance in autumn
It can be seen from the table that the ratio of nugget and still (1-
y / (yO + v)) were used to represent the spatial autocorrelation
level of regional random variables. The value less than 25%
indicated that the variable had a strong spatial correlation (Xia
et al., 2011). From the table, y0 / (yO + y) of chla concentration
was small in different resolutions, indicating that spatial
heterogeneity of chla concentration was mainly caused by the
structural factors, random factors played a secondary role. Chla
concentration showed similar structural in different scales of the
same season. In addition, the range of nugget and still changed
strongly in different seasons, indicating that there were different
structural of chla concentration in different seasons.
4.3 Spatial Scale Error in Different Seasons
In this paper different resolution remote sensing images were
used to study the differences of spatial scale. In order to reduce
error from parameters of different satellites, transit time and
other uncertainties factors, MODIS 250m, 500m images were
used to carry out scale error analysis. According to the formula
(5), combined with estimation formula (MODIS 250m of the
formula) of chla concentration, scale errors were calculated,
which caused by chla concentration inversion of coarse
resolution in different seasons.
