Species-specific aboveground biomass estimation using semantic segmentation of UAV photogrammetric point clouds
[EN] Accurate, species-specific estimation of aboveground biomass (AGB) at the individual plant level is essential for characterizing forest structure, supporting ecological and wildfire modelling, and enabling fine-scale carbon accounting. This study presents a methodological framework for estimati...
| Autores: | , , , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2026 |
| País: | España |
| Institución: | Universitat Politècnica de València (UPV) |
| Repositorio: | RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia |
| Idioma: | inglés |
| OAI Identifier: | oai:dnet:riunet______::6c8a71e4643fc0ef462604e7f9e0c228 |
| Acceso en línea: | https://riunet.upv.es/handle/10251/235585 |
| Access Level: | acceso abierto |
| Palabra clave: | Unmanned aerial vehicles (UAV) Digital aerial photogrammetry (DAP) Aboveground biomass (AGB) Class3Dp Species classification Mediterranean forests Point cloud classification Point cloud segmentation |
| Sumario: | [EN] Accurate, species-specific estimation of aboveground biomass (AGB) at the individual plant level is essential for characterizing forest structure, supporting ecological and wildfire modelling, and enabling fine-scale carbon accounting. This study presents a methodological framework for estimating species-specific AGB at individual plant level in Mediterranean ecosystems using UAV-based digital aerial photogrammetry (UAV-DAP). High-resolution point clouds were processed through a multi-step workflow including object-based segmentation, thirteen species classification and AGB regression modeling. The overall accuracy of species classification across six study areas was 81.6%, with a maximum of 89.9%. The regression models for AGB estimation yielded an average R2 of 0.69 across all species, highlighting species such as Anthyllis cytisoides (R2 = 0.83, RMSE = 0.07 kg, n = 47), Juniperus oxycedrus (R2 = 0.83, RMSE = 3.17 kg, n = 32); or Pinus halepensis (R2 = 0.77, RMSE = 11.79 kg, n = 20). These findings demonstrate the potential of UAV-DAP for practical estimates of AGB. The study underscores UAV-DAP as a cost-effective tool for forest management, ecological monitoring, and biomass assessments, paving the way for broader applications in environmental science and resource management. |
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