Above-ground biomass estimation from LiDAR data using random forest algorithms
Random forest (RF) models were developed to estimate the biomass for the Pinus radiata species in a region of the Basque Autonomous Community where this species has high cover, using the National Forest Inventory, allometric equations and low-density discrete LiDAR data. This article explores the tu...
| Autores: | , , , , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2022 |
| País: | España |
| Institución: | Universidad del País Vasco |
| Repositorio: | Addi. Archivo Digital para la Docencia y la Investigación |
| OAI Identifier: | oai:addi.ehu.eus:10810/61179 |
| Acceso en línea: | http://hdl.handle.net/10810/61179 |
| Access Level: | acceso abierto |
| Palabra clave: | LiDAR biomass regression random forest |
| Sumario: | Random forest (RF) models were developed to estimate the biomass for the Pinus radiata species in a region of the Basque Autonomous Community where this species has high cover, using the National Forest Inventory, allometric equations and low-density discrete LiDAR data. This article explores the tuning for RF hyperparameters, obtaining two models with an R2 higher than 0.7 using 2-fold cross-validation. The models selected were applied in Orozko, a municipality with more than 5000 ha of this species, where the model predicts a biomass of 1.06–1.08 Mton, which is between 16–18 % higher than the biomass predicted by the Basque Government. |
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