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

共 50 条

[1] Above-Ground Biomass and Biomass Components Estimation Using LiDAR Data in a Coniferous Forest
He, Qisheng
Chen, Erxue
An, Ru
Li, Yong
FORESTS, 2013, 4 (04) : 984 - 1002
[2] Estimation of Above-Ground Forest Biomass in Nepal by the Use of Airborne LiDAR, and Forest Inventory Data
Bahadur, K. C. Yam
Liu, Qijing
Saud, Pradip
Gaire, Damodar
Adhikari, Hari
LAND, 2024, 13 (02)
[3] Estimation of above-ground forest biomass using metrics based on Gaussian decomposition of waveform lidar data
Zhuang, Wei
Mountrakis, Giorgos
Wiley, John J., Jr.
Beier, Colin M.
INTERNATIONAL JOURNAL OF REMOTE SENSING, 2015, 36 (07) : 1871 - 1889
[4] Deep point cloud regression for above-ground forest biomass estimation from airborne LiDAR
Oehmcke, Stefan
Li, Lei
Trepekli, Katerina
Revenga, Jaime C.
Nord-Larsen, Thomas
Gieseke, Fabian
Igel, Christian
REMOTE SENSING OF ENVIRONMENT, 2024, 302
[5] COMPONENT FOREST ABOVE GROUND BIOMASS ESTIMATION USING LIDAR AND SAR DATA
Zeng, Peng
Shi, Jianmin
Huang, Jimao
Zhang, Yongxin
Zhang, Wangfei
2022 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS 2022), 2022, : 6395 - 6398
[6] Research on Rapeseed Above-Ground Biomass Estimation Based on Spectral and LiDAR Data
Jiang, Yihan
Wu, Fang
Zhu, Shaolong
Zhang, Weijun
Wu, Fei
Yang, Tianle
Yang, Guanshuo
Zhao, Yuanyuan
Sun, Chengming
Liu, Tao
AGRONOMY-BASEL, 2024, 14 (08):
[7] Above-ground biomass estimation of a secondary forest in Sarawak
Chai, F.Y.C.
Journal of Tropical Forest Science, 1997, 9 (03): : 359 - 368
[8] Above-ground biomass estimation using airborne discrete-return and full-waveform LiDAR data in a coniferous forest
Nie, Sheng
Wang, Cheng
Zeng, Hongcheng
Xi, Xiaohuan
Li, Guicai
ECOLOGICAL INDICATORS, 2017, 78 : 221 - 228
[9] Estimation of Voxel-Based Above-Ground Biomass Using Airborne LiDAR Data in an Intact Tropical Rain Forest, Brunei
Kim, Eunji
Lee, Woo-Kyun
Yoon, Mihae
Lee, Jong-Yeol
Son, Yowhan
Abu Salim, Kamariah
FORESTS, 2016, 7 (11):
[10] Estimating above-ground biomass of subtropical forest using airborne LiDAR in Hong Kong
Chan, Evian Pui Yan
Fung, Tung
Wong, Frankie Kwan Kit
SCIENTIFIC REPORTS, 2021, 11 (01)

← 1 2 3 4 5 →