Prediction of aboveground biomass from low-density LiDAR data: validation over P. radiata data from a region north of Spain

Tojal, L.-T., Bastarrika, A., Barrett, B. , Sanchez Espeso, J. M., Lopez-Guede, J. M. and Graña, M. (2019) Prediction of aboveground biomass from low-density LiDAR data: validation over P. radiata data from a region north of Spain. Forests, 10(9), 819. (doi: 10.3390/f10090819)

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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.

Item Type:Articles
Additional Information:The work reported in this paper was partially supported by FEDER funds for the MINECO project TIN2017-85827-P, and project KK-2018/00071 of the Elkartek 2018 funding program of the Basque Government. Additional support comes from grant IT1284-19 of the Basque Country.
Keywords:Forest biomass, LiDAR, remote sesing.
Glasgow Author(s) Enlighten ID:Barrett, Dr Brian
Creator Roles:
Barrett, B.Writing – original draft, Writing – review and editing
Authors: Tojal, L.-T., Bastarrika, A., Barrett, B., Sanchez Espeso, J. M., Lopez-Guede, J. M., and Graña, M.
Subjects:G Geography. Anthropology. Recreation > GB Physical geography
G Geography. Anthropology. Recreation > GE Environmental Sciences
College/School:College of Science and Engineering > School of Geographical and Earth Sciences
Journal Name:Forests
ISSN (Online):1999-4907
Published Online:19 September 2019
Copyright Holders:Copyright © 2019 The Authors
First Published:First published in Forests 10(9): 819
Publisher Policy:Reproduced under a Creative Commons License

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