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Open AccessArticle

Tree Biomass Equations from Terrestrial LiDAR: A Case Study in Guyana

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Laboratory of Geo-Information Science and Remote Sensing, Wageningen University & Research, Droevendaalsesteeg 3, 6708 PB Wageningen, The Netherlands
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Center for International Forestry Research (CIFOR), Jalan CIFOR, Situ Gede, Bogor Barat 16115, Indonesia
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CAVElab-Computational & Applied Vegetation Ecology, Ghent University, Coupure Links 653, 9000 Gent, Belgium
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Center for International Forestry Research (CIFOR) Germany, Charles-de-Gaulle-Strasse 5, 53113 Bonn, Germany
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Computing Sciences, Tampere University, Korkeakoulunkatu 7, 33720 Tampere, Finland
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Department of Geography, University College London, Gower Street, London WC1E 6BT, UK
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Guyana Forestry Commission (GFC), 1 Water Street, Kingston, Georgetown, Guyana
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Department of Forest Ecology and Management, Swedish University of Agricultural Sciences (SLU), Skogsmarksgränd, 901 83 Umeå, Sweden
*
Author to whom correspondence should be addressed.
Forests 2019, 10(6), 527; https://doi.org/10.3390/f10060527
Received: 1 May 2019 / Revised: 10 June 2019 / Accepted: 20 June 2019 / Published: 25 June 2019
(This article belongs to the Special Issue Remote Sensing Technology Applications in Forestry and REDD+)
Large uncertainties in tree and forest carbon estimates weaken national efforts to accurately estimate aboveground biomass (AGB) for their national monitoring, measurement, reporting and verification system. Allometric equations to estimate biomass have improved, but remain limited. They rely on destructive sampling; large trees are under-represented in the data used to create them; and they cannot always be applied to different regions. These factors lead to uncertainties and systematic errors in biomass estimations. We developed allometric models to estimate tree AGB in Guyana. These models were based on tree attributes (diameter, height, crown diameter) obtained from terrestrial laser scanning (TLS) point clouds from 72 tropical trees and wood density. We validated our methods and models with data from 26 additional destructively harvested trees. We found that our best TLS-derived allometric models included crown diameter, provided more accurate AGB estimates ( R 2 = 0.92–0.93) than traditional pantropical models ( R 2 = 0.85–0.89), and were especially accurate for large trees (diameter > 70 cm). The assessed pantropical models underestimated AGB by 4 to 13%. Nevertheless, one pantropical model (Chave et al. 2005 without height) consistently performed best among the pantropical models tested ( R 2 = 0.89) and predicted AGB accurately across all size classes—which but for this could not be known without destructive or TLS-derived validation data. Our methods also demonstrate that tree height is difficult to measure in situ, and the inclusion of height in allometric models consistently worsened AGB estimates. We determined that TLS-derived AGB estimates were unbiased. Our approach advances methods to be able to develop, test, and choose allometric models without the need to harvest trees. View Full-Text
Keywords: 3D tree modelling; aboveground biomass estimation; destructive sampling; Guyana; LiDAR; local tree allometry; model evaluation; quantitative structural model 3D tree modelling; aboveground biomass estimation; destructive sampling; Guyana; LiDAR; local tree allometry; model evaluation; quantitative structural model
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Lau, A.; Calders, K.; Bartholomeus, H.; Martius, C.; Raumonen, P.; Herold, M.; Vicari, M.; Sukhdeo, H.; Singh, J.; Goodman, R.C. Tree Biomass Equations from Terrestrial LiDAR: A Case Study in Guyana. Forests 2019, 10, 527.

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