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Remote Sens. 2016, 8(8), 653; doi:10.3390/rs8080653

Airborne Lidar Estimation of Aboveground Forest Biomass in the Absence of Field Inventory

1
Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA 91109, USA
2
Institut National de l’Information Géographique et Forestière (IGN) LaSTIG, MATIS, Université Paris-Est, Saint Mandé 94160, France
3
Institut de physique du globe de Paris, Unités Mixtes de Recherche (UMR) Centre National de la Recherche Scientifique (CNRS) 7154, Sorbonne Paris Cité Université Paris Diderot, Paris 75013, France
4
INESC-Coimbra and Department of Mathematic, University of Coimbra, Coimbra 3001-501, Portugal
5
Department of Natural Resources and Society, College of Natural Resources, University of Idaho, Moscow, ID 83843, USA
6
Forest Research Center, School of Agronomy, Universidade de Lisboa, Tapada da Ajuda, Lisbon 1349-017, Portugal
7
Escola Superior de Tecnologia e Gestão de Águeda, Universidade de Aveiro, Águeda 3754-909, Portugal
*
Author to whom correspondence should be addressed.
Academic Editors: L. Monika Moskal, Randolph H. Wynne and Prasad S. Thenkabail
Received: 30 April 2016 / Revised: 5 August 2016 / Accepted: 8 August 2016 / Published: 12 August 2016
(This article belongs to the Special Issue Remote Sensing of Vegetation Structure and Dynamics)
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Abstract

The scientific community involved in the UN-REDD program is still reporting large uncertainties about the amount and spatial variability of CO2 stored in forests. The main limitation has been the lack of field samplings over space and time needed to calibrate and convert remote sensing measurements into aboveground biomass (AGB). As an alternative to costly field inventories, we examine the reliability of state-of-the-art lidar methods to provide direct retrieval of many forest metrics that are commonly collected through field sampling techniques (e.g., tree density, individual tree height, crown cover). AGB is estimated using existing allometric equations that are fed by lidar-derived metrics at either the individual tree- or forest layer-level (for the overstory or underneath layers, respectively). Results over 40 plots of a multilayered forest located in northwest Portugal show that the lidar method provides AGB estimates with a relatively small random error (RMSE = of 17.1%) and bias (of 4.6%). It provides local AGB baselines that meet the requirements in terms of accuracy to calibrate satellite remote sensing measurements (e.g., the upcoming lidar GEDI (Global Ecosystem Dynamics Investigation), and the Synthetic Aperture Radar (SAR) missions NISAR (National Aeronautics and Space Administration and Indian Space Research Organization SAR) and BIOMASS from the European Space Agency, ESA) for AGB mapping purposes. The development of similar techniques over a variety of forest types would be a significant improvement in quantifying CO2 stocks and changes to comply with the UN-REDD policies. View Full-Text
Keywords: airborne laser scanning; lidar; 3D point cloud clustering; multi-layered forest structure; biomass; carbon; individual tree extraction; crown delineation; vegetation cover airborne laser scanning; lidar; 3D point cloud clustering; multi-layered forest structure; biomass; carbon; individual tree extraction; crown delineation; vegetation cover
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Ferraz, A.; Saatchi, S.; Mallet, C.; Jacquemoud, S.; Gonçalves, G.; Silva, C.A.; Soares, P.; Tomé, M.; Pereira, L. Airborne Lidar Estimation of Aboveground Forest Biomass in the Absence of Field Inventory. Remote Sens. 2016, 8, 653.

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