BEGIN:VCALENDAR VERSION:2.0 PRODID:-//pretalx//talks.osgeo.org//foss4g-2026//talk//K7W89F BEGIN:VTIMEZONE TZID:JST BEGIN:STANDARD DTSTART:20000101T000000 RRULE:FREQ=YEARLY;BYMONTH=1 TZNAME:JST TZOFFSETFROM:+0900 TZOFFSETTO:+0900 END:STANDARD END:VTIMEZONE BEGIN:VEVENT UID:pretalx-foss4g-2026-K7W89F@talks.osgeo.org DTSTART;TZID=JST:20260903T140000 DTEND;TZID=JST:20260903T143000 DESCRIPTION:1-Introduction and Study Area\nCladophora is a filamentous gree n alga native to the North American Great Lakes. Its excessive proliferati on not only causes foul odors and impairs public beach recreation but also triggers severe ecological issues\, including avian botulism outbreaks. S ince the 1990s\, the filtering effect of invasive species such as dreissen id mussels has significantly increased water clarity\, allowing sunlight t o penetrate to greater depths. This has led to massive Cladophora blooms e ven under relatively low nutrient concentrations. The study area of this r esearch focuses on the nearshore waters along the southern shore of Lake O ntario (the United States side). To achieve precise calibration of remote sensing observations\, the spatial scope of the study is strictly defined as two independent 6 km × 6 km square regions\, centered respectively aro und two key hydrological and biological monitoring stations established by the United States Geological Survey (USGS): the OIR station (Irondequoit\ , near Rochester) and the OOL station (Olcott).\nThese two core USGS stati ons provide substantial\, highly valuable ground-truth data for this study . These comprehensive datasets encompass multi-depth water flow velocities \, water turbidity\, and various critical chemical constituents in the wat er column (such as nutrient concentrations). More importantly\, the statio ns provide net weight data of Cladophora samples collected in situ across different depth gradients. These multi-dimensional\, high-precision ground truth indicators not only serve as an irreplaceable validation foundation for evaluating and calibrating various spectral remote sensing indices wi thin our open-source computational architecture\, but also enable us to de eply investigate the complex mechanisms underlying the relationships betwe en micro-environmental physicochemical variables and nearshore benthic alg al outbreaks.\n\n2-Evaluation of Traditional Indices and Experimental Deri vation of a Novel Index\nIn the preliminary remote sensing analysis phase\ , we developed a Python-based workflow to extract Sentinel-2 image bands a nd automatically calculated various traditional spectral indices\, includi ng NDVI\, FAI\, NDAVI\, and SABI. Statistical analysis of multi-temporal i magery (from May to August 2023) revealed that the mean and median values of these indices were frequently negative or extremely low\, accompanied b y disproportionately large standard deviations. For instance\, across mult iple summer observation dates\, the median values for NDVI and FAI consist ently hovered near zero (ranging from -0.012 to 0.025). At the same time\, NDAVI and SABI exhibited even deeper negative medians (often between -0.0 5 and -0.09). Furthermore\, the high standard deviations—frequently exce eding 0.25 for NDVI and 0.50 for SABI—demonstrated massive signal noise. This statistical analysis demonstrates that vegetation indices based on t he Near-Infrared (NIR) band exhibit severe absorption failures in aquatic environments\, rendering them inadequate for precise mapping of submerged benthic Cladophora.\nTo address this optical challenge and identify the op timal spectral response\, we designed a controlled physical experiment. A 3m × 3m water tank was used\, with an incandescent light source simulatin g solar irradiance. A receiver simulated the satellite sensor to capture r eflectance from a green surrogate representing benthic algae. Strikingly\, the experimental results revealed that the strongest reflectance signals emerged in the Blue and Short-Wave Infrared (SWIR) bands\, significantly d iverging from the band selections of traditional vegetation indices. Based on these empirical findings\, we are currently conducting rigorous mathem atical derivations utilizing the Blue and SWIR bands to formulate a novel\ , water-penetrating spectral index specifically optimized for Cladophora d etection.\n\n3-Automated Open-Source Cloud-Masking Algorithm to Bypass API Limitations \nTo achieve high-frequency monitoring of Cladophora\, we aim ed to build a fully open-source\, automated data acquisition architecture. However\, querying the Copernicus Data Space API inevitably encounters st rict request frequency limits and download volume quotas. Furthermore\, th e official API only provides the average cloud cover percentage at the ful l-scene level. For our small 6 km × 6 km Region of Interest (ROI)\, this macroscopic cloud assessment is highly inaccurate. A scene with a low aver age cloud percentage might still have dense clouds completely obscuring ou r study area\, leading to massive invalid downloads and wasted bandwidth. Additionally\, a single remote sensing image rarely covers the target area perfectly without clouds\, necessitating the seamless mosaicking of multi ple images and stricter screening for high-quality data.\nTo overcome this core bottleneck\, we designed and implemented a regional cloud-masking al gorithm based on image Quicklooks (previews) within our workflow. Since Qu icklook files are extremely small and consume negligible download bandwidt h\, the program automatically prioritizes retrieving them. Given that Quic klooks do not inherently contain geographic coordinates\, the algorithm fi rst extracts the boundary coordinates of the scene's footprint polygon fro m the metadata. Subsequently\, it correlates and standardizes the ROI's ge ographic coordinates against this footprint boundary. Based on this geomet ric translation\, the system can precisely reverse-engineer the specific p ixel rectangle corresponding to the study area on the unreferenced Quicklo ok image. Ultimately\, the algorithm computes the proportion of white pixe ls exclusively within this localized bounding box to accurately assess the true cloud cover rate within the ROI. Only when the ROI's cloud cover mee ts strict clear-sky thresholds does the system automatically trigger the A PI to download the heavy\, high-resolution original imagery. This algorith m successfully achieves precise "on-demand downloading\," effectively circ umventing API bandwidth restrictions while dramatically improving the effi ciency of acquiring the cloud-free data required for subsequent image mosa icking.\n\n4- Conclusion and Future Works \nThis study successfully establ ished a highly efficient\, Python-based open-source remote sensing downloa d architecture that practically circumvents API limitations. It also highl ighted the severe shortcomings of traditional vegetation indices through b oth satellite data statistics and controlled physical experiments. Future research will focus on advancing two primary tasks: \nFirst\, further refi ning the Quicklook-based cloud-masking algorithm to automate the acquisiti on of extensive multi-temporal imagery for seamless spatial mosaicking. To ensure complete reproducibility\, this process will be integrated into an end-to-end Python pipeline\, with the full source code made freely availa ble on GitHub.\nSecond\, finalizing the mathematical formulation of our no vel Blue-SWIR spectral index based on the water tank experiment\, and depl oying it within our open-source pipeline to precisely map the spatial dist ribution and evolutionary dynamics of Cladophora during peak summer blooms .\n\n\nReferences: \n[1] Howell\, E. T. (2018). A decadal-scale perspectiv e on the occurrence of Cladophora on the north shore of Lake Ontario. Envi ronmental Monitoring and Assessment. \n[2] Wright\, N.\, et al. (2024). Cl oudS2Mask: A novel deep learning approach for improved cloud and cloud sha dow masking in Sentinel-2 imagery. Remote Sensing of Environment\, 306\, 1 14122. \n[3] Copernicus Data Space Ecosystem. (2024). Quotas and Limitatio ns Documentation. DTSTAMP:20260717T234904Z LOCATION:Cosmos1 SUMMARY:Mapping Nearshore Cladophora in Lake Ontario: An Automated Open-Sou rce Sentinel-2 Workflow and Experimental Index Derivation - TonyLiu\, Shic hao Wang URL:https://talks.osgeo.org/foss4g-2026/talk/K7W89F/ END:VEVENT END:VCALENDAR