Optimizing canopy cover evaluation
This article introduces a comprehensive framework that uses LiDAR data and machine learning techniques to predict a range of biophysical variables. The framework is tested explicitly on Canopy Cover. The methodology combines optimized feature engineering, selection, and scalable parallel processing...
| Autores: | , , , |
|---|---|
| Tipo de recurso: | artículo |
| Fecha de publicación: | 2026 |
| País: | España |
| Institución: | Universitat Autònoma de Barcelona |
| Repositorio: | Dipòsit Digital de Documents de la UAB |
| Idioma: | inglés |
| OAI Identifier: | oai:dnet:uabarcelona_::3d3ebc8a499af55aeff2722d74406f99 |
| Acceso en línea: | https://ddd.uab.cat/record/328275 https://dx.doi.org/urn:doi:10.1016/j.envsoft.2026.106982 |
| Access Level: | acceso abierto |
| Palabra clave: | Canopy cover LiDAR data Machine learning |
| Sumario: | This article introduces a comprehensive framework that uses LiDAR data and machine learning techniques to predict a range of biophysical variables. The framework is tested explicitly on Canopy Cover. The methodology combines optimized feature engineering, selection, and scalable parallel processing to ensure both accuracy and computational efficiency. The study demonstrates the efficacy of AI-CanopyMapper in accurately predicting canopy cover. The full model achieved a mean absolute error (MAE) of 6.47% and an of 0.88. In contrast, the partial model, trained with only 1.3% of the available data, reached a MAE of approximately 15%, demonstrating strong generalization capabilities even under data-limited conditions. These results confirm that AI-CanopyMapper offers a fast, scalable, and data-efficient framework compared to traditional methods, highlighting its potential applications in forest management and environmental monitoring. |
|---|