GIS-Based Approach for Estimating Olive Tree Heights Using High-Resolution Satellite Imagery and Shadow Analysis
Source abstract
Abstract
Measuring tree heights is a critical step for assessing ecological and agricultural parameters, including biomass, carbon stock, and canopy volume. In extensive areas exceeding a few hectares, traditional terrestrial measurement methods are often prohibitively expensive in terms of time and cost. This study introduces a GIS-based methodology for estimating olive tree (Olea europaea L.) heights using very-high-resolution (VHR) satellite imagery. The approach integrates a mathematical model that incorporates slope and aspect information derived in a GIS environment from a large-scale Digital Elevation Model. By leveraging sun position data embedded in satellite image metadata, a dedicated geometric model was developed to calculate tree heights. Comparative analyses with a drone-based 3D model demonstrated the statistical reliability of the proposed methodology. While this study focuses on olive trees due to their unique canopy structure, the method could also be applied to other tree species or even to buildings and other vertically developed structures on the ground. Future developments aim to enhance efficiency and usability through the creation of a specialized GIS tool, making it a valuable resource for environmental monitoring, sustainable agricultural management, and broader spatial analysis applications.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Remote Sensing and LiDAR Applications · Remote Sensing in Agriculture · Soil erosion and sediment transport
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Engineering, University of Perugia, 06125 Perugia, Italy
Department of Civil, Constructional and Environmental Engineering, ‘La Sapienza’ University, 00185 Roma, Italy
Department of Agricultural, Food and Environmental Sciences, University of Perugia, 06121 Perugia, Italy
Funding
the project “PRECISOLIVO” under the 2014–2022 Rural Development Programme of the Umbria Region, Italy
References (41)
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