A Shadow Geometry Approach for Olive Tree Canopy Volume Estimation Using WorldView-3 Multispectral Imagery

Highlights: What are the main findings? A shadow geometry approach applied to a single WorldView-3 multispectral image enables the estimation of olive trees’ canopy height, area, and volume at the individual tree scale. Object-based classification on PCA-enhanced imagery provides the most reliable canopy and shadow delineation, achieving strong agreement with UAV-derived reference data. What are the implications of the main findings? The proposed workflow offers a scalable and cost-effective alternative to UAV and LiDAR surveys for trees’ structural characterization in precision agriculture. Single-acquisition VHR satellite imagery supports large-scale precision agriculture applications, including orchard monitoring, biomass estimation, and retrospective structural analyses. The accurate estimation of tree canopy volume is fundamental in precision agriculture for quantifying vegetation structure, biomass, and productivity in perennial cropping systems. This study investigates a shadow geometry approach for estimating olive tree canopy volumes from a single, very high-resolution WorldView-3 multispectral image. The method integrates multispectral classification for canopy and shadow delineation with a geometric model that infers canopy height from shadow measurements, accounting for solar position and terrain morphology. Two classification strategies were evaluated: object-based image analysis (OBIA) and pixel-based (PB) classification, each applied to the original eight-band multispectral image and to a derived dataset enriched with vegetation indices (NDVI—Normalized Difference Vegetation Index; NDRE—Normalized Difference Red Edge Index) and principal component analysis (PCA) components. The canopy volume was estimated by integrating classified canopy and shadow areas with shadow-derived canopy height. The methodology was tested in a Mediterranean olive orchard and validated against UAV-derived point clouds for approximately 700 trees. The results indicate that the approach captures spatial variability in canopy structure. The Object-Based Image Analysis (OBIA) applied to filtered PCA-enhanced imagery achieved the highest accuracy in canopy volume estimation (RMSE = 2.04 m3; R2 = 0.56), outperforming the alternative pixel-based (PB) classification applied to the original multispectral data. Overall, the study demonstrates the potential of single-image WorldView-3 data for rapid and scalable three-dimensional canopy characterization in precision agriculture.