The use of Vegetation Indices (VIs) is important in remote sensing for inferring biophysical parameters and monitoring environmental processes. Since VIs are derived from reflectance measurements, the scale and platform of acquisition-satellite, commercial multispectral drone, or simulation model—can potentially influence their derived values and inter-relationships. This study addresses this influence by presenting a multiscale comparison of four common indices: MPRI, NDVI, NDWI, and OSAVI. Data were sourced from three distinct platforms: Sentinel-2 satellite imagery, high-resolution data acquired via a multispectral drone, and a simulated spectral library generated using the PROSPECT-4 via ARTMO software. The core objective was to assess the consistency of the correlational structure among these VIs across the three scale measurements. Results indicate a statistically significant similarity in the overall correlational pattern of the indices, suggesting that the intrinsic mathematical relationships between these VIs are largely scale-invariant. However, the study identifies a critical need to standardize the data acquisition and processing protocol for drone measurements to match the known consistency of the Sentinel-2 mission and the rigorous parameters of the PROSPECT-4 simulation. This standardization is essential for future multi-sensor integration and calibration efforts.

Mangrove forests play a critical role in protecting tropical coastlines, storing carbon, and supporting marine life, yet mapping their fine-scale canopy structure remains difficult. This study introduces a structured ground-truthing strategy for automatic tree crown delineation of mangrove forests using high-resolution UAV imagery and deep learning. UAV-derived RGB orthomosaics from the Batangas coastline, Philippines, were manually annotated into three canopy classes: Individual Crowns (IC), Crown Clusters (CC), and Canopy Gaps (CG), which form the reference foundation for model training. A multi-head DeepLabV3–ResNet50 network was developed to jointly predict canopy masks and distance maps that capture crown geometry and spatial relationships. From these distance surfaces, local maxima were extracted as potential crown centers, and a Voronoi-style watershed segmentation was applied to delineate discrete crown units. An adaptive merging rule, based on peak prominence and spatial proximity, refined the segmentation by merging coalescent crowns while preserving distinct individuals. The results show interpretable delineation of crowns across varying canopy densities and environmental contexts, from isolated individuals in coastal fringes to coalescent crowns in mature stands. High-prominence peaks corresponded to dominant crowns, while moderate peaks represented subordinate or merging individuals. The framework integrates manual annotation with automation through deep learning models, establishing a reproducible foundation for crown-level mapping and long-term monitoring of mangrove canopy dynamics.