Reply to Comment on ‘A first map of tropical Africa’s above-ground biomass derived from satellite imagery’

By Alessandro Baccini, N. Laporte, Scott J. Goetz, M. Sun, H. Dong. Environmental Research Letters, 2008

301 citations recorded by the source corpus.

Biomass mapping using satellite imagery is a rapidly evolving field that has been greatly facilitated in recent years by the advent of LiDAR remote sensing coupled with co-located field measurements. The biomass map of Africa that we published in 2008 did not take direct advantage of coincident field and LiDAR measurements, as our more recent efforts have. The criticisms of our earlier map by Mitchard et al (2011 Environ. Res. Lett. 6 049001) are duly noted and worthwhile, although they are also limited in several respects that we describe. Most notably, they assess our map with field data sets that are only representative of a subset of conditions across the study domain, thus they not only inadequately characterize undisturbed tropical forest regions but also the diverse disturbance dynamics that are captured in satellite imagery. We point out the limitations of their assessment and focus on a way forward, moving beyond both inadequate field sampling and remote sensing to an approach the captures the full range of dynamics by directly coupling field and satellite measurements.

Subfields: Environmental Engineering, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/5828e1c2c3840da7afc69ef36c956c49f4b1a869

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Environmental Engineering

In Global and Planetary Change

Citations in the corpus

Allometric equation for predicting aboveground biomass of three tree species (2011, background): “As a consequence of the scarcity of data on tree biomass in central Africa, most of the current estimates of the carbon stocks in central Africa (Baccini et al. 2008, Lewis et al. 2009) are paradoxically based on pantropical biomass equations that do not rely on any data from Africa.”
Random forests and stochastic gradient boosting for predicting tree canopy cover: Comparing tuning processes and model performance (2016, methodology): “Baccini et al. (2008) used RF to map aboveground biomass in tropical Africa from satellite imagery.”
Mapping and monitoring deforestation and forest degradation in Sumatra (Indonesia) using Landsat time series data sets from 1990 to 2010 (2012, methodology): “Canopy height was useful for assessing differences between primary intact and degraded forest classes and the HOME metric was also used given previous evidence of its relationship to forest canopy structure and aboveground biomass (Drake et al 2002a, Baccini et al 2008).”