This paper assesses the reflectance difference values between the respective spectral bands in the visible and near-infrared (VNIR) of Sentinel 2A/2B Multi-Spectral Instrument (MSI) and Landsat 8/9 Operational Land Imager (OLI) sensors for seagrass, algae, and mixed species discrimination and monitoring in a shallow marine environment southeast of Bahrain Island in the Arabian Gulf. To achieve these, a field survey was conducted to collect samples of seawater, underwater sediments, seagrass (Halodule uninervis and Halophila stipulacea), and algae (green and brown). In addition, an experimental mode was established in a goniometric laboratory to simulate the marine environment, and spectral measurements were performed using an Analytical Spectral Devices (ASD) spectroradiometer. Measured spectra and their transformation using the continuum-removed reflectance spectral (CRRS) approach were analyzed to assess spectral separability among separate or mixed species at varying coverage rates. Afterward, the spectra were resampled and convolved in the solar-reflective spectral bands of MSI and OLI sensors and converted into water vegetation indices (WVIs) to investigate the potential of red, green, and blue bands for seagrass and algae species discrimination. The results of spectral and CRRS analyses highlighted the importance of the blue, green, and near-infrared (NIR) wavelengths for seagrass and algae detection and likely discrimination based on hyperspectral measurements. However, when resampled and convolved in MSI and OLI bands, spectral information loses the specific and unique absorption features and becomes more generalized and less precise. Therefore, relying on the multispectral bandwidth of MSI and OLI sensors, it is difficult or even impossible to differentiate or to map seagrass and algae individually at the species level. Instead of the red band, the integration of the blue or the green band in WVI increases their power to discriminate submerged aquatic vegetation (SAV), particularly the water adjusted vegetation index (WAVI), water enhanced vegetation index (WEVI), and water transformed difference vegetation index (WTDVI). These results corroborate the spectral and the CRRS analyses. However, despite the power of blue wavelength to penetrate deeper into the water, it also leads to a relative overestimation of dense SAV coverage due to more scattering in this part of the spectrum. Furthermore, statistical fits (p<0.05) between the reflectance in the respective VNIR bands of MSI and OLI revealed excellent linear relationships (R² of 0.999) with insignificant root mean square difference (RMSD) (≤ 0.0015). Important agreement (0.63 ≤ R² ≤ 0.96) was also obtained between respective WVI regardless of the integrated spectral bands (i.e., red, green, and blue), yielding insignificant RMSD (≤ 0.01). Accordingly, these results pointed out that MSI and OLI sensors are spectrally similar, and their data can be used jointly to monitor accurately the spatial distribution of SAV and its dynamic in time and space in shallow marine environments, provided that rigorous data pre-processing issues are addressed.

Monitoring the long-term cumulative impacts of industrial mining and environmental changes in sensitive ecosystems remains a critical challenge for sustainable resource management. This study investigates 41 years (1984–2025) of land use and land cover (LULC) dynamics in the Athabasca oil sands region (Alberta, Canada) using the historical Landsat archive (TM, ETM+, and OLI). The methodology integrated temporal and spectral features through two complementary phases: automated spectral trajectory-based change detection and a Support Vector Machine (SVM) classification combining original bands with multiple spectral indices, including normalized difference vegetation index (NDVI), soil adjusted vegetation index (SAVI), enhanced vegetation index (EVI), transformed difference vegetation index (TDVI), bare soil index (BSI), and Modified Normalized Difference Water Index (MNDWI). This framework quantified baseline LULC, mining expansion, tailings pond footprints, and reclamation operations across a 12,146-km² study area. The spectral trajectory analysis effectively tracked multiple temporal disturbances, revealing accelerating mining activities alongside forest harvesting, wildfire stress, and insect damage. Additionally, it captured progressive and significant spectral variations in the Athabasca River’s surface water, reflecting cumulative industrial runoff and long-term environmental pressure. Concurrently, the SVM classifications (Overall accuracy ≥ 96.8% and Kappa coefficient ≥ 94%) measured major LULC shifts. Dense coniferous forests declined by 1200 km², while mixed forests decreased from 4500 km² in 1984 to a minimum of 1200 km² in 2010, before recovering to 3040 km² by 2025 due to reclamation efforts. Conversely, active mining infrastructure and tailings ponds expanded from 95 km² (1984) to 900 km² (2025), occupying 7.41% of the landscape. Ultimately, this integrated remote sensing approach captured the complex interactions between accelerating industrial development, climate stressors, and the localized progress of land reclamation, providing a scalable framework for ecosystem monitoring in heavily disturbed landscapes.