Mapping Algal Blooms in Aquatic Ecosystems Using Long-Term Landsat Data: A Case Study of Yuqiao Reservoir from 1984–2022

• Published in: Remote sensing (Basel, Switzerland) Vol. 15; no. 17; p. 4317
• Main Authors: Liu, Dandan, Ding, Hu, Han, Xingxing, Lang, Yunchao, Chen, Wei
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01-09-2023
• Subjects: Algae; Algal blooms; Anthropogenic factors; Aquatic ecosystems; Aquatic plants; Case studies; Correlation coefficient; Correlation coefficients; Datasets; Decision trees; Earth resources technology satellites; Eutrophication; Freshwater resources; human factors; Human influences; Industrial pollution; Lakes; Landsat; Landsat satellites; long-term trends; Mapping; Methods; Performance evaluation; Phytoplankton; Purification; Remote sensing; Reservoirs; Satellite imagery; Spatial distribution; Temporal variations; Vegetation; Water; Water bodies; water eutrophication; Water levels; Water purification; Water quality; Yuqiao Reservoir; Canada; China
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs15174317

Water eutrophication poses a dual threat to ecological and human well-being. Gaining insight into the intricate dynamics of phytoplankton bloom phenology holds paramount importance in comprehending the complexities of aquatic ecosystems. Remote sensing technologies have gained attention for mapping algal blooms (ABs) effectively, but distinguishing them from aquatic vegetation (AV) remains challenging due to their similar spectral characteristics. To address this issue, we propose a meticulous three-step methodology for AB mapping employing long-term Landsat imagery. Initially, a multi-index decision tree model (DTM) is deployed to identify the vegetation signal (VS) encompassing both AV and ABs. Subsequently, the annual maximum growth range of AV is precisely delineated using vegetation presence frequency (VPF) in conjunction with normal and low water level imagery. Lastly, ABs are accurately extracted by inversely intersecting VS and AV. The performance of our approach is thoroughly validated using the interclass correlation coefficient (ICC) based on a Gaofen-2 Panchromatic Multi-spectral (GF-2 PMS) image, demonstrating strong consistency with notable values of 0.822 longitudinally, 0.771 latitudinally, and 0.797 overall. The method is applied to Landsat images from 1984 to 2022 to quantify the spatial distribution and temporal variations of ABs in Yuqiao Reservoir—a significant national water body spanning a vast area of 135 km2 in China. Our findings reveal a pervasive and uneven dispersion of ABs, predominantly concentrated in the northern sector. Notably, the intensity of ABs experienced an initial surge from 1984 to 2008, followed by a subsequent decline from 2014 to 2022. Importantly, anthropogenic activities, such as fish cage culture, alongside pollution stemming from nearby industrial and agricultural sources, exert a profound influence on the dynamics of water eutrophication. Fortunately, governmental initiatives focused on water purification exhibit commendable efficacy in mitigating the ecological burden on reservoirs and upholding water quality. The methodological framework presented in this study boasts remarkable precision in AB extraction and exhibits considerable potential in addressing the needs of aquatic ecosystems.