Environmental monitoring systems require robust uncertainty quantification for effective decision-making in complex ecological processes. Harmful algal blooms represent a critical challenge where prediction uncertainty directly impacts resource allocation and response timing, yet current remote sensing-based prediction systems provide only deterministic classifications without confidence measures. This gap between algorithmic predictions and actionable risk assessment limits operational utility for stakeholders managing water quality under varying risk tolerances. This study developed a transferable probabilistic forecasting framework integrating Sentinel-2 multispectral imagery with quantile regression and ensemble machine learning to generate continuous confidence indicators for cyanobacteria density prediction, demonstrated through its application to Lake Okeechobee, Florida. The methodology combines spectral indices extracted from Sentinel-2 data with XGBoost for quantile regression at 0.05, 0.50, and 0.95 probability levels, and LightGBM for multi-horizon temporal forecasting. Sentinel-2’s 13 spectral bands spanning visible to shortwave infrared wavelengths, combined with its 5-day revisit frequency provide a spectrally rich and temporally dense input space that is well-suited to gradient boosting methods such as XGBoost, which can exploit complex nonlinear interactions among spectral features to distinguish cyanobacterial signatures from background water constituents. LightGBM achieved mean absolute percentage errors of 2.9% for 10-day forecasts and 5.7% for 20-day forecasts, outperforming conventional regression models. The framework generates 90% prediction intervals that enable reliable risk classifications for operational bloom management. This approach bridges the gap between satellite-based algal bloom detection and actionable decision-making by quantifying predictive uncertainty, representing a shift from binary classifications to probability-based environmental monitoring systems that accommodate varying stakeholder risk tolerances in water quality management applications.

In numerous applications, including land planning and global warming studies, the mapping of changes in land use and land cover (LULC) on a regional scale is crucial. LULC changes can occasionally interfere with the climate and natural processes, which have an adverse impact on regional patterns. In this study, we use remote sensing and geographic information systems (GIS) to analyze these LULC changes in the Raichur district of Karnataka. In the first stage of this investigation, data from Landsat 8 collected in December 2015 are subjected to a supervised classification algorithm. With the help of ground control points retrieved from Google Earth Pro, also dated December 2015, the maximum likelihood approach is used to classify the image using the six bands of the Landsat image. The data of the shift in land use and land cover are examined in depth in the 34study’s second phase. The third step concludes with a map that shows how the land cover has changed. The complexity and dynamics of changes in the region’s land use and land cover are better understood, thanks to this tripartite approach. <!----> <!----> <!----> <!---->

Environmental monitoring of freshwater ecosystems faces significant challenges in detecting illicit wastewater discharges, which often remain unrecognized due to their intermittent nature and limited spatial footprint. This study presents a novel integrated strategy combining satellite remote sensing, Unmanned Aerial Vehicle (UAV)-based proximal sensing, and in situ measurements to enhance pollution detection in vulnerable aquatic environments. The methodology was applied to Lake Avernus (Italy), a volcanic lake historically affected by eutrophication and toxic cyanobacterial blooms. Landsat 8–9 thermal analysis revealed no detectable anomalies, reflecting the limitations of its coarse spatial resolution. Sentinel-2 multispectral imagery was then analyzed through spectral indices, band ratios, and reflectance signatures, revealing localized variations in surface reflectance and spatial heterogeneity in water optical properties. These satellite-derived anomalies guided targeted high-resolution UAV surveys. UAV-based thermal imaging revealed an elevated-temperature zone along the adjacent shoreline. In situ field screening flagged a candidate chemical anomaly at this location. The hierarchical framework demonstrates that satellite screening effectively identifies areas of concern, while UAV thermal imaging enables high-resolution localization of features invisible to satellite sensors, and in situ measurements provide essential ground-truth validation. This replicable, low-cost methodology offers a powerful tool for early warning, surveillance, and sustainable management of sensitive freshwater ecosystems.