Next Article in Journal
Hedonic Analysis of Forest Pest Invasion: the Case of Emerald Ash Borer
Previous Article in Journal
Mapping Tree Species Composition Using OHS-1 Hyperspectral Data and Deep Learning Algorithms in Changbai Mountains, Northeast China
Previous Article in Special Issue
Linking Terrestrial LiDAR Scanner and Conventional Forest Structure Measurements with Multi-Modal Satellite Data
Open AccessArticle

Prediction of Aboveground Biomass from Low-Density LiDAR Data: Validation over P. radiata Data from a Region North of Spain

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
Computational Intelligence Group, University of the Basque Country UPV/EHU, Paseo Manuel Lardizabal, 20018 San Sebastian, Spain
School of Geographical and Earth Sciences, University of Glasgow, G128QQ Glasgow, Scotland
Department of Geographic Engineering and Graphic Expression Techniques, School of Civil Engineering, University of Cantabria, Avda. de los Castros, 44, 39005 Santander, Spain
Author to whom correspondence should be addressed.
Forests 2019, 10(9), 819;
Received: 26 July 2019 / Revised: 11 September 2019 / Accepted: 17 September 2019 / Published: 19 September 2019
Estimation of forestry aboveground biomass (AGB) by means of aerial Light Detection and Ranging (LiDAR) data uses high-density point sampling data obtained in dedicated flights, which are often too costly for available research budgets. In this paper we exploit already existing public low-density LiDAR data obtained for other purposes, such as cartography. The challenge is to show that such low-density data allows accurate biomass estimation. We demonstrate the approach on data available from plantations of Pinus radiata in the Arratia-Nervión region, located in Biscay province located in the North of Spain. We use public data gathered from the low-density (0.5 pulse/m2) LiDAR flight conducted by the Basque Government in 2012 for cartographic production. We propose a linear regression model based on explanatory variables obtained from the LiDAR point cloud data. We calibrate the model using field data from the Fourth National Forest Inventory (NFI4), including the selection of the optimal model variables. The results revealed that the best model depends on two variables extracted from LiDAR data: One directly related with tree height and a second parameter with the canopy density. The model explained 80% of its variability with a standard error of 0.25 ton/ha in logarithmic units. We validate the predictions against the biomass measurements provided by the government institutions, obtaining a difference of 8%. The proposed approach would allow the exploitation of the periodic available low-density LiDAR data, collected with territorial and cartographic purposes, for a more frequent and less expensive control of the forestry biomass. View Full-Text
Keywords: aboveground biomass; LiDAR; linear regression; Pinus radiata aboveground biomass; LiDAR; linear regression; Pinus radiata
Show Figures

Figure 1

MDPI and ACS Style

Tojal, L.-T.; Bastarrika, A.; Barrett, B.; Sanchez Espeso, J.M.; Lopez-Guede, J.M.; Graña, M. Prediction of Aboveground Biomass from Low-Density LiDAR Data: Validation over P. radiata Data from a Region North of Spain. Forests 2019, 10, 819.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

Search more from Scilit
Back to TopTop