Above-ground biomass estimation from LiDAR data using random forest algorithms
•The paper explores Random Forest models to estimate the biomass.•In the paper real data of Pinus Radiata species in a specific area of the Basque Country (Spain) were used.•The LiDAR data used to create the models are of low-density•The dataset used to create the models has a very small size.•Two m...
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| Published in | Journal of computational science Vol. 58; p. 101517 |
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| Main Authors | , , , , |
| Format | Journal Article |
| Language | English |
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Elsevier B.V
01.02.2022
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| Abstract | •The paper explores Random Forest models to estimate the biomass.•In the paper real data of Pinus Radiata species in a specific area of the Basque Country (Spain) were used.•The LiDAR data used to create the models are of low-density•The dataset used to create the models has a very small size.•Two models were obtained showing an R2 higher than 0.7 and a prediction between 16–18 % higher than those given by the Basque Government.
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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| AbstractList | •The paper explores Random Forest models to estimate the biomass.•In the paper real data of Pinus Radiata species in a specific area of the Basque Country (Spain) were used.•The LiDAR data used to create the models are of low-density•The dataset used to create the models has a very small size.•Two models were obtained showing an R2 higher than 0.7 and a prediction between 16–18 % higher than those given by the Basque Government.
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. |
| ArticleNumber | 101517 |
| Author | Boyano, Ana Bastarrika, Aitor Graña, Manuel Lopez-Guede, Jose Manuel Torre-Tojal, Leyre |
| Author_xml | – sequence: 1 givenname: Leyre surname: Torre-Tojal fullname: Torre-Tojal, Leyre email: leyre.torre@ehu.es organization: Department of Mining and Metallurgical Engineering and Materials Science, Faculty of Engineering, University of the Basque Country (UPV/EHU), Nieves Cano 12, 01006 Vitoria-Gasteiz, Spain – sequence: 2 givenname: Aitor surname: Bastarrika fullname: Bastarrika, Aitor organization: Department of Mining and Metallurgical Engineering and Materials Science, Faculty of Engineering, University of the Basque Country (UPV/EHU), Nieves Cano 12, 01006 Vitoria-Gasteiz, Spain – sequence: 3 givenname: Ana surname: Boyano fullname: Boyano, Ana organization: Mechanical Engineering Department, Faculty of Engineering of Vitoria-Gasteiz, University of the Basque Country, UPV/EHU, Nieves Cano 12, 01006 Vitoria-Gasteiz, Spain – sequence: 4 givenname: Jose Manuel surname: Lopez-Guede fullname: Lopez-Guede, Jose Manuel organization: Department of Systems Engineering and Automatic Control, Faculty of Engineering, University of the Basque Country (UPV/EHU), Nieves Cano 12, 01006 Vitoria-Gasteiz, Spain – sequence: 5 givenname: Manuel surname: Graña fullname: Graña, Manuel organization: Department of Computer Science and Artificial Intelligence, Faculty of Computer Science, University of the Basque Country (UPV/EHU), Paseo Manuel De Lardizabal, 1, 20018 Donostia-San Sebastian, Spain |
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| Title | Above-ground biomass estimation from LiDAR data using random forest algorithms |
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