Above-ground biomass estimation from LiDAR data using random forest algorithms

被引：57

作者：

Torre-Tojal, Leyre ^{[1
]}

Bastarrika, Aitor ^{[1
]}

Boyano, Ana ^{[2
]}

Manuel Lopez-Guede, Jose ^{[3
,5
]}

Grana, Manuel ^{[4
,5
]}

机构：

[1] Univ Basque Country, Fac Engn, UPV EHU, Dept Min & Met Engn & Mat Sci, Nieves Cano 12, Vitoria 01006, Spain

[2] Univ Basque Country, Fac Engn Vitoria Gasteiz, Mech Engn Dept, UPV EHU, Nieves Cano 12, Vitoria 01006, Spain

[3] Univ Basque Country, UPV EHU, Dept Syst Engn & Automat Control, Fac Engn, Nieves Cano 12, Vitoria 01006, Spain

[4] Univ Basque Country, Fac Comp Sci, UPV EHU, Dept Comp Sci & Artificial Intelligence, Paseo Manuel De Lardizabal 1, Donostia San Sebastian 20018, Spain

[5] Univ Basque Country, Computat Intelligence Grp, UPV EHU, Vitoria, Spain

来源：

JOURNAL OF COMPUTATIONAL SCIENCE | 2022年 / 58卷

关键词：

LiDAR; Biomass; Regression; Random forest; RADIATA D. DON; AIRBORNE LIDAR; DISCRETE-RETURN; GROUND BIOMASS; TREE; HEIGHT; VOLUME; COVER; EQUATIONS; QUICKBIRD;

D O I：

10.1016/j.jocs.2021.101517

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

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 R-2 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.

引用

页数：14

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