deterioration. This chapter focuses on harmful algal blooms in Lake Taihu (China),
as a case study, demonstrating the potential of remote sensing for integrated
assessment of watershed dynamics. The temporal and spatial variability of the
conditions in Lake Taihu and its watershed were derived from satellite data to
produce a monthly time series of algal bloom coverage, aquatic vegetation extent,
and land cover from 2000 to 2013. Environmental features related to nutrient
loading, climate conditions, and agricultural practices were also used to analyze
the driving forces of algal blooms. Two distinct temporal patterns were identified.
Prior to 2006, bloom initiation date was sensitive to agricultural activities (winter
crop productivity and nutrient loading). After 2006–2007, an inversion of this
relationship was observed, suggesting nutrient saturation with a shift to other
watershed scale stressors, mainly climate related. After 2009, a return to pre-2006
conditions was shown. These results demonstrate how remote sensing can be used
to monitor watershed dynamics as a whole, especially in conjunction with in situ
environmental data.
Keywords Algal blooms • Aquatic vegetation • Lake Taihu • Land cover • Remote
sensing • Water quality
1 Introduction
Inland water quality and consequently human health can be strongly affected by
human activities on a watershed scale. An integrated assessment and identification
of water quality stressors are needed to understand interactions and relations
between land use, human activities, and water quality. Remote sensing can deliver
a wide range of spatial–temporal information on environmental dynamics, making
it a fundamental tool for monitoring and management of complex ecosystems [1].
Lake Taihu, the third largest freshwater lake in China, serves as a primary
drinking water source for 40 million people in the Jiangsu and Zhejiang provinces
as well as the Shanghai municipality [2]. However, the Taihu watershed has
undergone tremendous change (driven by anthropic activities) during the past two
decades. In 1990, algal blooms caused the closure of 116 factories and the cessation
of water supplies to three million people in Wuxi [3]. In May 2007, a massive
cyanobacterial bloom of Microcystis led to water supply problems for over one
million people. These events brought the algal blooms of Lake Taihu to both
national and international attention [2, 4–6].
The increased occurrence of harmful algal blooms (HABs), mainly caused by
cyanobacteria, is a global issue and a serious threat to inland freshwater ecosystems.
Many genera of cyanobacteria produce toxins with adverse effects on human health
and aquatic life [7] and posing a threat to drinking and irrigation water supplies
[8]. Algal blooms have often been associated with eutrophication due to excessive
inputs of nutrients [9–11]. Nutrient inflow management actions have been put into
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P. Villa et al.
as a case study, demonstrating the potential of remote sensing for integrated
assessment of watershed dynamics. The temporal and spatial variability of the
conditions in Lake Taihu and its watershed were derived from satellite data to
produce a monthly time series of algal bloom coverage, aquatic vegetation extent,
and land cover from 2000 to 2013. Environmental features related to nutrient
loading, climate conditions, and agricultural practices were also used to analyze
the driving forces of algal blooms. Two distinct temporal patterns were identified.
Prior to 2006, bloom initiation date was sensitive to agricultural activities (winter
crop productivity and nutrient loading). After 2006–2007, an inversion of this
relationship was observed, suggesting nutrient saturation with a shift to other
watershed scale stressors, mainly climate related. After 2009, a return to pre-2006
conditions was shown. These results demonstrate how remote sensing can be used
to monitor watershed dynamics as a whole, especially in conjunction with in situ
environmental data.
Keywords Algal blooms • Aquatic vegetation • Lake Taihu • Land cover • Remote
sensing • Water quality
1 Introduction
Inland water quality and consequently human health can be strongly affected by
human activities on a watershed scale. An integrated assessment and identification
of water quality stressors are needed to understand interactions and relations
between land use, human activities, and water quality. Remote sensing can deliver
a wide range of spatial–temporal information on environmental dynamics, making
it a fundamental tool for monitoring and management of complex ecosystems [1].
Lake Taihu, the third largest freshwater lake in China, serves as a primary
drinking water source for 40 million people in the Jiangsu and Zhejiang provinces
as well as the Shanghai municipality [2]. However, the Taihu watershed has
undergone tremendous change (driven by anthropic activities) during the past two
decades. In 1990, algal blooms caused the closure of 116 factories and the cessation
of water supplies to three million people in Wuxi [3]. In May 2007, a massive
cyanobacterial bloom of Microcystis led to water supply problems for over one
million people. These events brought the algal blooms of Lake Taihu to both
national and international attention [2, 4–6].
The increased occurrence of harmful algal blooms (HABs), mainly caused by
cyanobacteria, is a global issue and a serious threat to inland freshwater ecosystems.
Many genera of cyanobacteria produce toxins with adverse effects on human health
and aquatic life [7] and posing a threat to drinking and irrigation water supplies
[8]. Algal blooms have often been associated with eutrophication due to excessive
inputs of nutrients [9–11]. Nutrient inflow management actions have been put into
86
P. Villa et al.