With the acceleration in the development process of industrial and agricultural modernization and urbanization, human activities have intensified the eutrophication of lakes in surrounding cities, leading to the frequent occurrence of cyanobacterial blooms (Deng et al., 2016; Zhang et al., 2018). At present, algal blooms have become one of the most serious and challenging ecological environmental problems (Huisman et al., 2018). Numerous large lakes around the world have seen severe cyanobacterial blooms, such as Lake Taihu (Qin et al., 2015) and Lake Chaohu in China (Shi et al., 2013) and Winnipeg Lake in Canada (Schindler et al., 2012). Frequent cyanobacterial blooms not only worsen the sensory properties of water bodies but also damage the overall balance of the lake ecosystem and have a serious impact on human health, residential life, and economic development (Shi et al., 2017; Zhou et al., 2008). According to the environmental consensus, there is a strong link between the frequent occurrence of cyanobacterial blooms and the economic process, population growth, and industrial and agricultural production of surrounding cities (Deng et al., 2016; Paerl et al., 2014). Nevertheless, there is still a lack of quantitative research on the correlation between them, which mainly stems from the neglect of the cumulative effect on the development of cyanobacteria in the advancement process of urbanization. Therefore, studying the response of cyanobacterial blooms to urban development and climate change can advance the research paradigm of cyanobacteria influencing factors, effectively predict the growth trend of cyanobacterial bloom, optimize the spatial development pattern between cities and lakes, and provide an important decision-making basis for the sustainable development of urban planning and industrial layout.
The formation and evolution of cyanobacterial blooms are affected by human activities, the climate, the environment, and other factors. The interaction among these factors results in the complex temporal and spatial variation of cyanobacterial blooms. Previous studies focused on the trend variation of cyanobacteria caused by meteorological and environmental factors. Specifically, Huang et al. (2020) used the general regression neural network model to determine the complex relationship between harmful algal blooms and environmental factors and revealed the regional magnitude of harmful algal blooms and related driving factors. Walls et al. (2018) found that when the temperature was higher than 18°C, the rise of water temperature would promote the growth of cyanobacteria. However, changes in the form and scale of human activities can also significantly affect the growth of cyanobacteria. Under the background of huge social and economic changes, it is essential to study the dynamics of cyanobacterial blooms in surrounding lakes and their response to population aggregation and socioeconomic development. Generally, the interaction between natural and anthropogenic factors significantly influences lake changes. However, the impact of human factors is weakened by the prominent effect of natural factors and the lagging and long-term influence of anthropogenic factors. These factors can be studied from the perspective of determining how cyanobacteria respond to human activities and climate factors with different intensities. Thus, it is necessary to select typical cases with significant variation caused by urban development to study the dynamic rules of cyanobacterial blooms.
We chose the research objects based on the measurement criteria of the development speed of urban economy and the change of the distance between cities and lakes. There are 27 lakes in China with a surface area larger than 500 km2 (Ma et al., 2010). As a typical central lake, Lake Chaohu, located in the middle of Anhui Province, is the primary source of drinking water for Hefei city (HF) and Chaohu city (Qin et al., 2013). It has an important geographical location and ecological strategic position. In recent years, the construction of Binhu New District, which belongs to Baohe District (BH), has gradually shortened the distance between Lake Chaohu and Hefei, making Hefei a real lakeside city. Moreover, Hefei city, with one of the fastest rates of economic growth in the world in recent decades, has had a significant effect on Lake Chaohu. Taking Lake Chaohu and the economic belt around Lake Chaohu as the sample, we analyzed the response of cyanobacterial blooms in Lake Chaohu to urban expansion, economic growth, population aggregation, and climate change from a multi-dimensional perspective, which is conducive to revealing the relationship between water pollution and urban development and providing support for the optimization and adjustment of industrial structure and spatial layout.
Field sampling and laboratory measurements are the main conventional methods to obtain information on cyanobacteria (Oyama et al., 2015). However, field sampling can only be used for local surveys, with limited sampling frequency, high cost, and increased time, and when the spatial and temporal scale is large, it is finite to disclose the information of cyanobacterial blooms (Kutser, 2004; Wang et al., 2011). At present, 12 monitoring sites are employed to observe the cyanobacteria in Lake Chaohu. However, because of the limited sampling sites and short time span, the data have low spatial and temporal resolutions. In addition, we need to extract information from the entire lake to reveal the long-term response of cyanobacteria dynamics to urban development and climate change on a broader spatial scale. Satellite remote sensing technology can provide multi-temporal, multi-scale, and multi-spectral observation information of cyanobacterial blooms in the same area, which can quickly, effectively, and dynamically reflect its spatiotemporal characteristics through the analysis and processing of remote sensing image data (Vincent et al., 2004; Wu et al., 2016). This tool can compensate for the limitations of traditional monitoring methods and has become an essential technical means for dynamic monitoring of the water environment, providing valid spatiotemporal resolution for monitoring variation in inland, coastal waters, and various ecosystem (Hu et al., 2010; Mu et al., 2020; Palmer et al., 2015).
The Normalized Difference Vegetation Index (NDVI) is obtained by a nonlinear combination of the near-infrared band and the red band from remote sensing images (Jarchow et al., 2017). Because the spectral characteristics of the outbreak area of cyanobacterial blooms are similar to that of green vegetation (Zhu et al., 2010), the NDVI can be used as an effective index to extract the information on cyanobacterial blooms. Recently, the NDVI has been successfully applied to explore the temporal and spatial dynamics of algae. Kiage and Walker (2009) used the NDVI to monitor duckweed and floating vegetation in Lake Maracaibo, Venezuela and confirmed the tremendous potential of the NDVI in monitoring floating vegetation. Ma et al. (2021) employed the NDVI method to extract information on harmful algal blooms in Lake Chaohu, with an accuracy of 96.1%. Vijay et al. (2016) defined the NDVI as an index of algae density and assessed the correlation between the NDVI and Normalized Difference Water Index (NDWI). In a word, the NDVI can effectively reflect the information on cyanobacteria in lakes and can combine cyanobacteria dynamics, human activities, and climate changes for research.
This study focused on the dynamic variations of cyanobacterial bloom and their response to urban development and climate change in Lake Chaohu. The main contributions of this study are as follows: first, we observed the ecological environment of cyanobacterial blooms in lake from the perspectives of economic growth, population aggregation, and climate change, which advances the knowledge development of the complex interaction among the influencing factors of cyanobacteria. Second, taking Lake Chaohu as a typical case, we aimed to reveal the annual and seasonal spatiotemporal variations of cyanobacterial blooms and to verify the significant impact of different factors on cyanobacteria by constructing a statistical analysis framework integrating remote sensing data, socio-economic data, and meteorological data. Third, we determined the response level of cyanobacterial blooms in Lake Chaohu and the Baohe Lake region to economic growth in different regions by considering the difference in economic growth in different areas. In general, the analysis results of cyanobacterial bloom variation trend with urban development provide useful insights for optimizing the industrial pattern and urban layout in this region and for strengthening the cognition of the coupling relationship between urban development and the lake environment.