1204 downscaling high-resolution data is scientifically preferable to upscaling older coarse-resolution imagery, as aggregation preserves real observations, whereas interpolation artificially introduces spatial detail that was never measured. This distinction is particularly critical for tailings facilities, which exhibit strong spatial heterogeneity, sharp boundaries, and dynamic interfaces between water, dry tailings, and vegetation. Upscaling coarse-resolution data would fragment mixed pixels into artificially sharpened patterns, leading to false precision and misleading change detection. By contrast, downscaling preserves consistent mixedpixel behavior across all sensors, enabling robust interpretation of true spatiotemporal dynamics and supporting reliable multi-decadal change analysis of tailings facility evolution. Figure 3 – Resampling (Upscaling and Downscaling) Spectral analysis represents a core methodology in remote sensing, focusing on the interpretation of interactions between electromagnetic radiation and Earth surface materials to derive information about their physical and environmental properties. Variations in surface reflectance across different spectral wavelengths provide a quantitative basis for identifying, characterizing, and tracking surface features and their temporal evolution. Within this analytical context, spectral indices constitute a powerful means of extracting thematic information from multispectral satellite imagery. These indices are formulated through mathematical relationships between reflectance values recorded in selected spectral bands and are specifically designed to highlight particular surface characteristics. Spectral indices are widely applied in land cover mapping, vegetation and water monitoring, change detection, mineral and resource assessment, and environmental impact analysis. By evaluating the intensity and relative behavior of spectral responses, these indices support the differentiation and interpretation of vegetation, surface water, and other land cover components. When applied to multi-band satellite datasets, spectral indices enable consistent spatial mapping and long-term monitoring of tailings storage facilities, facilitating the detection of surface dynamics, spatial patterns, and temporal changes associated with facility operation and reclamation. For the analysis of the tailings storage facility evolution over the 50-year study period, the Normalized Difference Vegetation Index (NDVI) and the Normalized Difference Water Index (NDWI) were selected as the most appropriate and reliable indicators. NDVI (Eq. 1) is extensively used to quantify vegetation presence, vigor, and spatial distribution, making it particularly suitable for identifying reclaimed surfaces, assessing vegetation establishment, and evaluating the effectiveness of progressive rehabilitation measures. NDWI (Eq. 2) is highly responsive to surface water and moisture conditions and allows accurate delineation of decantation ponds, saturated tailings zones, and variations in water extent linked to operational and environmental processes. A key advantage of using NDVI and NDWI lies in their reliance on spectral bands within the visible and near-infrared regions that are consistently available across the Landsat sensor series (MSS, TM, ETM+, and OLI) as well as the Sentinel-2 mission. This spectral continuity enables consistent multi-decadal index calculation and ensures comparability between historical and contemporary satellite observations. The combined use of NDVI and NDWI provides long-term monitoring of vegetation dynamics and water-related processes within tailings facilities, supporting reliable interpretation of spatiotemporal changes over a 50-year period.
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