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International Journal of Forestry Research
Volume 2014 (2014), Article ID 715796, 14 pages
Review Article

Remote Sensing of Aboveground Biomass in Tropical Secondary Forests: A Review

1Department of Ecology, Institute of Biosciences, University of São Paulo, 05508-090 São Paulo, SP, Brazil
2Sustainability Science Program, Kennedy School of Government, Harvard University, Cambridge, MA 02138, USA

Received 3 December 2013; Revised 1 February 2014; Accepted 12 February 2014; Published 23 March 2014

Academic Editor: Guy R. Larocque

Copyright © 2014 J. M. Barbosa et al. 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.


Tropical landscapes are, in general, a mosaic of pasture, agriculture, and forest undergoing various stages of succession. Forest succession is comprised of continuous structural changes over time and results in increases in aboveground biomass (AGB). New remote sensing methods, including sensors, image processing, statistical methods, and uncertainty evaluations, are constantly being developed to estimate biophysical forest changes. We review 318 peer-reviewed studies related to the use of remotely sensed AGB estimations in tropical forest succession studies and summarize their geographic distribution, sensors and methods used, and their most frequent ecological inferences. Remotely sensed AGB is broadly used in forest management studies, conservation status evaluations, carbon source and sink investigations, and for studies of the relationships between environmental conditions and forest structure. Uncertainties in AGB estimations were found to be heterogeneous with biases related to sensor type, processing methodology, ground truthing availability, and forest characteristics. Remotely sensed AGB of successional forests is more reliable for the study of spatial patterns of forest succession and over large time scales than that of individual stands. Remote sensing of temporal patterns in biomass requires further study, in particular, as it is critical for understanding forest regrowth at scales useful for regional or global analyses.