Estimating aboveground biomass in tropical forests: Field methods and error analysis for the calibration of remote sensing observations - Gonçalves, Fábio Guimarães; Treuhaft, Robert N.; Law, Beverly; Almeida, André Quintão; Walker, Wayne S.; Baccini, Alessandro; Santos, João Roberto dos; Graça, Paulo Maurício Lima Alencastro de (Remote Sensing, 2017)
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. © 2017 by the authors; licensee MDPI, Basel, Switzerland.
Keywords: Biology; Biomass; Calibration; Data Acquisition; Error Analysis; Errors; Forestry; Tropics; Uncertainty Analysis; Allometry; Amazon; Error Propagation; Forest Inventory; Icesat/glas; Uncertainty; Remote Sensing