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Impact of data model and point density on aboveground forest biomass estimation from airborne LiDAR.

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posted on 09.01.2018, 15:25 by Mariano Garcia, Sassan Saatchi, Antonio Ferraz, Carlos Alberto Silva, Susan Ustin, Alexander Koltunov, Heiko Balzter
BACKGROUND: Accurate estimation of aboveground forest biomass (AGB) and its dynamics is of paramount importance in understanding the role of forest in the carbon cycle and the effective implementation of climate change mitigation policies. LiDAR is currently the most accurate technology for AGB estimation. LiDAR metrics can be derived from the 3D point cloud (echo-based) or from the canopy height model (CHM). Different sensors and survey configurations can affect the metrics derived from the LiDAR data. We evaluate the ability of the metrics derived from the echo-based and CHM data models to estimate AGB in three different biomes, as well as the impact of point density on the metrics derived from them. RESULTS: Our results show that differences among metrics derived at different point densities were significantly different from zero, with a larger impact on CHM-based than echo-based metrics, particularly when the point density was reduced to 1 point m-2. Both data models-echo-based and CHM-performed similarly well in estimating AGB at the three study sites. For the temperate forest in the Sierra Nevada Mountains, California, USA, R2 ranged from 0.79 to 0.8 and RMSE (relRMSE) from 69.69 (35.59%) to 70.71 (36.12%) Mg ha-1 for the echo-based model and from 0.76 to 0.78 and 73.84 (37.72%) to 128.20 (65.49%) Mg ha-1 for the CHM-based model. For the moist tropical forest on Barro Colorado Island, Panama, the models gave R2 ranging between 0.70 and 0.71 and RMSE between 30.08 (12.36%) and 30.32 (12.46) Mg ha-1 [between 0.69-0.70 and 30.42 (12.50%) and 61.30 (25.19%) Mg ha-1] for the echo-based [CHM-based] models. Finally, for the Atlantic forest in the Sierra do Mar, Brazil, R2 was between 0.58-0.69 and RMSE between 37.73 (8.67%) and 39.77 (9.14%) Mg ha-1 for the echo-based model, whereas for the CHM R2 was between 0.37-0.45 and RMSE between 45.43 (10.44%) and 67.23 (15.45%) Mg ha-1. CONCLUSIONS: Metrics derived from the CHM show a higher dependence on point density than metrics derived from the echo-based data model. Despite the median of the differences between metrics derived at different point densities differing significantly from zero, the mean change was close to zero and smaller than the standard deviation except for very low point densities (1 point m-2). The application of calibrated models to estimate AGB on metrics derived from thinned datasets resulted in less than 5% error when metrics were derived from the echo-based model. For CHM-based metrics, the same level of error was obtained for point densities higher than 5 points m-2. The fact that reducing point density does not introduce significant errors in AGB estimates is important for biomass monitoring and for an effective implementation of climate change mitigation policies such as REDD + due to its implications for the costs of data acquisition. Both data models showed similar capability to estimate AGB when point density was greater than or equal to 5 point m-2.


Mariano Garcia is supported by the Marie Curie International Outgoing Fellowship within the 7th European Community Framework Programme (ForeStMap-3D Forest Structure Monitoring and Mapping, Project Reference: PIOF-GA-2013-629376). The contents of this paper reflect solely the authors’ views and not the views of the European Commission. This study was partially funded by the Natural Environment Research Council’s support for the National Centre for Earth Observation. H. Balzter was supported by the Royal Society Wolfson Research Merit Award, 2011/R3 and the NERC National Centre for Earth Observation. This research was carried out within the ambit of the project “Multi-sensor remote sensing study of California’s Rim Fire to inform post-fire ecosystem restoration and effective prevention of future catastrophic wildfires” funded by USDA Forest Service, as the main sponsor, and University of California Davis under Cost Share Agreement 10-IA-11130400-009.



Carbon Balance and Management, 2017, 12:4

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/Organisation/COLLEGE OF SCIENCE AND ENGINEERING/School of Geography, Geology and the Environment/GIS and Remote Sensing


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Carbon Balance and Management


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The datasets supporting the conclusions of this article are available from the authors upon request.