Wed, Aug 17, 2022: 5:00 PM-6:30 PM
ESA Exhibit Hall
Background/Question/MethodsIncreases in cyanobacterial blooms across many of the world’s lakes pose growing risks to human health and aquatic ecosystems. There is therefore an urgent need to improve our understanding of phytoplankton distribution, abundance, and dynamics in freshwater lakes via monitoring efforts. While field-based, in situ monitoring is commonly used to assess phytoplankton communities via chlorophyll-a (chl-a) abundance, such monitoring often fails to capture their dynamic temporal and spatial patterns. Remotely sensed data from satellites are capable of capturing aquatic water quality conditions at broader spatial scales and higher temporal resolutions than is possible with in situ measurements alone, allowing us to ask questions about how key water quality parameters like chl-a change across multiple spatial and temporal scales within and among lakes. To investigate and characterize the dynamics of chlorophyll-a in this fashion, we calibrated satellite imagery from the Landsat constellation of sensors with in situ chl-a data retrieved from publicly available databases dating back to the 1980s, and mapped chl-a across a subset of lakes in Maine with the aim to a) examine changes in spatiotemporal patterns of chl-a abundance through time, and b) investigate connections between spatiotemporal chl-a patterns, lake surface water temperature, and land cover features.
Results/ConclusionsIntra-lake heterogeneity in chl-a was high, ranging over several orders of magnitude. This result underscores the importance of accounting for strong spatiotemporal variability of remotely sensed lake water parameters. For example, the robustness of algorithms, i.e., strength of relationships between in situ and Landsat-derived chl-a values, was sensitive to both the size of the buffer from which pixels were extracted and the location of the in situ calibration point on the lake. Using the best-fitting algorithm calibrated for Maine lakes to estimate chl-a across lake surfaces, we detected significant spatial clustering of chl-a within lakes. Preliminary results demonstrate that patterns of within-lake chl-a clustering varied across years, with certain regions of the lake showing hot spots of chl-a in some years, but not others. Further, chl-a and Landsat-derived surface water temperature were strongly associated in some regions, but the associations were spatially variable and the strength of correlations depended on the region of the lake, ranging from highly correlated in some regions, to not significant in others. These scaling challenges are important when interpreting analyses integrating remote sensing and in situ measurements, and in scaling up estimates of chl-a from pixels and regions within a lake to whole lakes.
Results/ConclusionsIntra-lake heterogeneity in chl-a was high, ranging over several orders of magnitude. This result underscores the importance of accounting for strong spatiotemporal variability of remotely sensed lake water parameters. For example, the robustness of algorithms, i.e., strength of relationships between in situ and Landsat-derived chl-a values, was sensitive to both the size of the buffer from which pixels were extracted and the location of the in situ calibration point on the lake. Using the best-fitting algorithm calibrated for Maine lakes to estimate chl-a across lake surfaces, we detected significant spatial clustering of chl-a within lakes. Preliminary results demonstrate that patterns of within-lake chl-a clustering varied across years, with certain regions of the lake showing hot spots of chl-a in some years, but not others. Further, chl-a and Landsat-derived surface water temperature were strongly associated in some regions, but the associations were spatially variable and the strength of correlations depended on the region of the lake, ranging from highly correlated in some regions, to not significant in others. These scaling challenges are important when interpreting analyses integrating remote sensing and in situ measurements, and in scaling up estimates of chl-a from pixels and regions within a lake to whole lakes.