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

[31] Above-ground biomass estimation of Indian tropical forests using X band Pol-InSAR and Random Forest
Yadav, Sadhana
Padalia, Hitendra
Sinha, Sanjiv K.
Srinet, Ritika
Chauhan, Prakash
REMOTE SENSING APPLICATIONS-SOCIETY AND ENVIRONMENT, 2021, 21
[32] Exploring the Inclusion of Small Regenerating Trees to Improve Above-Ground Forest Biomass Estimation Using Geospatial Data
Le, Anh, V
Paull, David J.
Griffin, Amy L.
REMOTE SENSING, 2018, 10 (09)
[33] Above-ground biomass estimation in a Mediterranean sparse coppice oak forest using Sentinel-2 data
Moradi, Fardin
Sadeghi, Seyed Mohamad Moein
Heidarlou, Hadi Beygi
Deljouei, Azade
Boshkar, Erfan
Borz, Stelian Alexandru
ANNALS OF FOREST RESEARCH, 2022, 65 (01) : 165 - 182
[34] Optimal Support Vector Machines for Forest Above-ground Biomass Estimation from Multisource Remote Sensing Data
Guo, Ying
Li, Zengyuan
Zhang, Xu
Chen, Er-xue
Bai, Lina
Tian, Xin
He, Qisheng
Feng, Qi
Li, Wenmen
2012 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS), 2012, : 6388 - 6391
[35] On spatial variability of above-ground forest biomass
Tajchman, S
Benyon, R
Bren, L
Kochenderfer, J
Pan, CS
BIOMASS & BIOENERGY, 1996, 11 (05): : 383 - 386
[36] A Validated and Accurate Method for Quantifying and Extrapolating Mangrove Above-Ground Biomass Using LiDAR Data
Salum, Rafaela B.
Robinson, Sharon A.
Rogers, Kerrylee
REMOTE SENSING, 2021, 13 (14)
[37] Estimating Above-Ground Biomass of Araucaria angustifolia (Bertol.) Kuntze Using LiDAR Data
Rex, Franciel Eduardo
Dalla Corte, Ana Paula
Machado, Sebastiao do Amaral
Silvan, Carlos Alberto
Sanquetta, Carlos Roberto
FLORESTA E AMBIENTE, 2019, 26 (04):
[38] Detection of large above-ground biomass variability in lowland forest ecosystems by airborne LiDAR
Jubanski, J.
Ballhorn, U.
Kronseder, K.
Franke, J.
Siegert, F.
BIOGEOSCIENCES, 2013, 10 (06) : 3917 - 3930
[39] MAPPING FOREST ABOVE-GROUND BIOMASS AND ITS CHANGES FROM LVIS WAVEFORM DATA
Huang, Wenli
Sun, Guoqing
Dubayah, Ralph
Zhang, Zhiyu
Ni, Wenjian
2012 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS), 2012, : 6561 - 6564
[40] InSAR-based estimation of forest above-ground biomass using phase histogram technique
Wu, Chuanjun
Shen, Peng
Tebaldini, Stefano
Liao, Mingsheng
Zhang, Lu
INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION, 2025, 136

← 1 2 3 4 5 →