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Remote Sens. 2017, 9(1), 47; doi:10.3390/rs9010047

Estimating Aboveground Biomass in Tropical Forests: Field Methods and Error Analysis for the Calibration of Remote Sensing Observations

1
Canopy Remote Sensing Solutions, Florianópolis, SC 88032, Brazil
2
Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA 91109, USA
3
Department of Forest Ecosystems & Society, Oregon State University, Corvallis, OR 97331, USA
4
Departamento de Engenharia Agrícola, Universidade Federal de Sergipe, SE 49100, Brazil
5
Woods Hole Research Center, Falmouth, MA 02540, USA
6
National Institute for Space Research (INPE), São José dos Campos, SP 12227, Brazil
7
Department of Environmental Dynamics, National Institute for Research in Amazonia (INPA), Manaus, AM 69067, Brazil
*
Author to whom correspondence should be addressed.
Academic Editors: Guangxing Wang, Erkki Tomppo, Dengsheng Lu, Huaiqing Zhang, Qi Chen, Lars T. Waser and Prasad S. Thenkabail
Received: 1 September 2016 / Revised: 20 December 2016 / Accepted: 28 December 2016 / Published: 7 January 2017
(This article belongs to the Special Issue Digital Forest Resource Monitoring and Uncertainty Analysis)
View Full-Text   |   Download PDF [2868 KB, uploaded 7 January 2017]   |  

Abstract

Mapping and monitoring of forest carbon stocks across large areas in the tropics will necessarily rely on remote sensing approaches, which in turn depend on field estimates of biomass for calibration and validation purposes. Here, we used field plot data collected in a tropical moist forest in the central Amazon to gain a better understanding of the uncertainty associated with plot-level biomass estimates obtained specifically for the calibration of remote sensing measurements. In addition to accounting for sources of error that would be normally expected in conventional biomass estimates (e.g., measurement and allometric errors), we examined two sources of uncertainty that are specific to the calibration process and should be taken into account in most remote sensing studies: the error resulting from spatial disagreement between field and remote sensing measurements (i.e., co-location error), and the error introduced when accounting for temporal differences in data acquisition. We found that the overall uncertainty in the field biomass was typically 25% for both secondary and primary forests, but ranged from 16 to 53%. Co-location and temporal errors accounted for a large fraction of the total variance (>65%) and were identified as important targets for reducing uncertainty in studies relating tropical forest biomass to remotely sensed data. Although measurement and allometric errors were relatively unimportant when considered alone, combined they accounted for roughly 30% of the total variance on average and should not be ignored. Our results suggest that a thorough understanding of the sources of error associated with field-measured plot-level biomass estimates in tropical forests is critical to determine confidence in remote sensing estimates of carbon stocks and fluxes, and to develop strategies for reducing the overall uncertainty of remote sensing approaches. View Full-Text
Keywords: forest inventory; allometry; uncertainty; error propagation; Amazon; ICESat/GLAS forest inventory; allometry; uncertainty; error propagation; Amazon; ICESat/GLAS
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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

Gonçalves, F.; Treuhaft, R.; Law, B.; Almeida, A.; Walker, W.; Baccini, A.; dos Santos, J.R.; Graça, P. Estimating Aboveground Biomass in Tropical Forests: Field Methods and Error Analysis for the Calibration of Remote Sensing Observations. Remote Sens. 2017, 9, 47.

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