anthropogenic activities and algal blooms in Lake Taihu, where GDP was found to
be strongly correlated to the initial bloom date (R
2
¼ 0.99), and GDP per capita was
correlated to annual bloom duration (R
2
¼ 0.75). These findings imply that anthropogenic factors may substantially outweigh climatic factors. The GDP in the Lake
Taihu watershed increased from 848 billion Yuan (RMB) in 1998 to 2,662 billion
Yuan (RMB) in 2007 and the GDP per capita from 2 Â 10
4 to 6 Â 10
4 Yuan (RMB)
(not adjusted for inflation) [2]. Correspondingly, the number of months of detected
algal blooms increased from two in 1998 to ten in 2007, and the initial bloom date
advanced more than 100 days. Given the expected growth in human activity in this
area in the coming decades, urgent efforts are needed to better identify the causes of
Lake Taihu’s degradation.
4.4 Integrated Assessment
Integrating remotely sensed and in situ data of the Taihu watershed, a multi-temporal
analysis of HAB dynamics (maximum bloom area, mean bloom area, and bloom
starting date) was performed. In situ data (temperature, nutrients, and chlorophyll-a
concentration) and remotely sensed environmental factors (land cover change and
agricultural practices as a proxy of anthropogenic influence) were used.
The available data covered two periods: for 2000–2010, a full set of environmental data was available (including in situ data on nutrient concentrations); and
for the 2011–2013 temporal range, only features derived from satellite data and
meteorological data. Figure 14 shows the mean monthly profiles of Lake Taihu
covering the temporal range of 11 years, from 2000 to 2010, for some environmental parameters derived from in situ data—mean monthly air temperature (T ),
chlorophyll-a concentration (Chl-a), dissolved total nitrogen (TN), and dissolved
total phosphorus (TP)—compared with corresponding mean monthly profiles of
information extracted from MODIS satellite data for agricultural crop phenology
(in terms of NDVI monthly mean) and mean monthly algal bloom areal extent.
From monthly profiles, the environmental dynamics in Lake Taihu and its
watershed indicate that:
• Temperature cycles were characterized by very hot summers (mean monthly
temperature for July and August ~30
C) and relatively cold winters (mean
monthly temperature for January ~5
C), with a notable variability in spring
temperature and in hot season duration (T > 20
C).
• TN concentrations were highest in the winter (maximum loadings observed from
2004 to 2008), with decreasing TN concentrations in the late spring (May–June)
and increasing TN concentrations in the autumn (October–November); TP
concentrations increased in the summer (July–September), with particular maxima in recent years (2004, 2005, 2006, 2009).
• Chl-a concentrations, as a proxy of phytoplankton abundance, were lowest in the
winter (December to March) and highest in the spring and summer, which in
some cases could be attributed to cyanobacterial blooms.
Using Remote Sensing to Assess the Impact of Human Activities on Water. . .
103
be strongly correlated to the initial bloom date (R
2
¼ 0.99), and GDP per capita was
correlated to annual bloom duration (R
2
¼ 0.75). These findings imply that anthropogenic factors may substantially outweigh climatic factors. The GDP in the Lake
Taihu watershed increased from 848 billion Yuan (RMB) in 1998 to 2,662 billion
Yuan (RMB) in 2007 and the GDP per capita from 2 Â 10
4 to 6 Â 10
4 Yuan (RMB)
(not adjusted for inflation) [2]. Correspondingly, the number of months of detected
algal blooms increased from two in 1998 to ten in 2007, and the initial bloom date
advanced more than 100 days. Given the expected growth in human activity in this
area in the coming decades, urgent efforts are needed to better identify the causes of
Lake Taihu’s degradation.
4.4 Integrated Assessment
Integrating remotely sensed and in situ data of the Taihu watershed, a multi-temporal
analysis of HAB dynamics (maximum bloom area, mean bloom area, and bloom
starting date) was performed. In situ data (temperature, nutrients, and chlorophyll-a
concentration) and remotely sensed environmental factors (land cover change and
agricultural practices as a proxy of anthropogenic influence) were used.
The available data covered two periods: for 2000–2010, a full set of environmental data was available (including in situ data on nutrient concentrations); and
for the 2011–2013 temporal range, only features derived from satellite data and
meteorological data. Figure 14 shows the mean monthly profiles of Lake Taihu
covering the temporal range of 11 years, from 2000 to 2010, for some environmental parameters derived from in situ data—mean monthly air temperature (T ),
chlorophyll-a concentration (Chl-a), dissolved total nitrogen (TN), and dissolved
total phosphorus (TP)—compared with corresponding mean monthly profiles of
information extracted from MODIS satellite data for agricultural crop phenology
(in terms of NDVI monthly mean) and mean monthly algal bloom areal extent.
From monthly profiles, the environmental dynamics in Lake Taihu and its
watershed indicate that:
• Temperature cycles were characterized by very hot summers (mean monthly
temperature for July and August ~30
C) and relatively cold winters (mean
monthly temperature for January ~5
C), with a notable variability in spring
temperature and in hot season duration (T > 20
C).
• TN concentrations were highest in the winter (maximum loadings observed from
2004 to 2008), with decreasing TN concentrations in the late spring (May–June)
and increasing TN concentrations in the autumn (October–November); TP
concentrations increased in the summer (July–September), with particular maxima in recent years (2004, 2005, 2006, 2009).
• Chl-a concentrations, as a proxy of phytoplankton abundance, were lowest in the
winter (December to March) and highest in the spring and summer, which in
some cases could be attributed to cyanobacterial blooms.
Using Remote Sensing to Assess the Impact of Human Activities on Water. . .
103