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

[11] Above-Ground Biomass Estimation of Plantation with Different Tree Species Using Airborne LiDAR and Hyperspectral Data
Gao, Linghan
Chai, Guoqi
Zhang, Xiaoli
REMOTE SENSING, 2022, 14 (11)
[12] Satellite estimation of tropical secondary forest above-ground biomass: data from Brazil and Bolivia
Steininger, MK
INTERNATIONAL JOURNAL OF REMOTE SENSING, 2000, 21 (6-7) : 1139 - 1157
[13] Estimation of Above-Ground Biomass for Dendrocalamus Giganteus Utilizing Spaceborne LiDAR GEDI Data
Yang, Huanfen
Qin, Zhen
Shu, Qingtai
Xu, Li
Yu, Jinge
Luo, Shaolong
Wu, Zaikun
Xia, Cuifen
Yang, Zhengdao
IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING, 2025, 18 : 5271 - 5286
[14] Estimating above-ground biomass of subtropical forest using airborne LiDAR in Hong Kong
Evian Pui Yan Chan
Tung Fung
Frankie Kwan Kit Wong
Scientific Reports, 11
[15] Above ground biomass estimation in an African tropical forest with lidar and hyperspectral data
Laurin, Gaia Vaglio
Chen, Qi
Lindsell, Jeremy A.
Coomes, David A.
Del Frate, Fabio
Guerriero, Leila
Pirotti, Francesco
Valentini, Riccardo
ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING, 2014, 89 : 49 - 58
[16] Improved country-wide estimation of above-ground tropical forest biomass using locally calibrated GEDI spaceborne LiDAR data
Zhou, Yuchuan
Taylor, David M.
Tang, Hao
ENVIRONMENTAL RESEARCH LETTERS, 2025, 20 (01):
[17] Remote sensing estimation of forest above-ground biomass based on spaceborne lidar ICESat-2/ATLAS data
Song, Hanyue
Shu, Qingtai
Xi, Lei
Qiu, Shuang
Wei, Zhiyue
Yang, Zezhi
Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering, 2022, 38 (10): : 191 - 199
[18] Estimation of above-ground biomass using machine learning approaches with InSAR and LiDAR data in tropical peat swamp forest of Brunei Darussalam
Zadbagher, Elaheh
Marangoz, Aycan Murat
Becek, Kazimierz
IFOREST-BIOGEOSCIENCES AND FORESTRY, 2024, 17 : 172 - 179
[19] Estimation of above ground biomass in boreal forest using ground-based Lidar
Taheriazad, L.
Moghadas, H.
Sanchez-Azofeifa, A.
3RD INTERNATIONAL CONFERENCE ON ADVANCES IN ENVIRONMENT RESEARCH, 2017, 2017, 68
[20] Optimal Combination of Predictors and Algorithms for Forest Above-Ground Biomass Mapping from Sentinel and SRTM Data
Chen, Lin
Wang, Yeqiao
Ren, Chunying
Zhang, Bai
Wang, Zongming
REMOTE SENSING, 2019, 11 (04)

← 1 2 3 4 5 →