PS 43-121 - El Niño-associated changes in LiDAR-derived LAI and leaf area profiles in an eastern Amazonian forest

Friday, August 12, 2016
ESA Exhibit Hall, Ft Lauderdale Convention Center
Marielle N. Smith1, Eronaldo de Oliveira2, Mauricio Ferreira3, Scott C. Stark4, Raimundo C. Oliveira5, Michela Figueira2, Plinio B. Camargo3, Luiz Aragao6 and Scott R. Saleska7, (1)Ecology & Evolutionary Biology, University of Arizona, Tucson, AZ, (2)Universidade Federal do Oeste do Pará, (3)Cena, University of Sao Paulo, (4)Department of Forestry, Michigan State University, East Lansing, MI, (5)Brazilian Agricultural Research Corporation (EMBRAPA), Brazil, (6)Instituto Nacional de Pesquisas Espaciais, (7)Department of Ecology and Evolutionary Biology, University of Arizona, Tucson, AZ

Amazon forests play an important role in global climate, and changes to these forests could alter cycles of water and carbon, and energy balance. Some coupled carbon-climate models predict forest dieback of the Amazon in response to climate changes, while others project forest persistence. Discrepancies are predominantly due to uncertainties about how vegetation will respond to climate changes, as well as variations in current and future climates. The 2015–2016 El Niño event provided an excellent opportunity to make real time measurements of tropical forest response to a severe and prolonged drought.

Understanding patterns of forest canopy structural change over seasonal and drought time series can help identify mechanisms by which forests respond to and influence climate. As such, leaf area index (LAI) is a key parameter in many ecosystem models that predict forest response to climate, while vertical canopy structure may help reveal additional mechanisms. We compared the seasonal pattern of LAI and vertical canopy structure between two baseline years and the 2015-2016 El Niño drought in the Tapajós National Forest, Brazil (Km67) using a multi-temporal ground-based LiDAR (Light Detection and Ranging) dataset.


In baseline years (2010 and 2012-2013), LiDAR-derived LAI estimates showed modest seasonal variation. In contrast, LAI estimates dropped substantially during the three months that coincided with the peak of the 2015-2016 El Niño. LAI declined at all levels of the canopy, with the magnitude of the decline decreasing with height. Coincident leaf litter measurements allow time series of leaf loss and leaf growth to be estimated at the canopy level, allowing us to address whether the dramatic decline in LAI in 2015-2016 is due to higher litterfall or lower leaf production.

Despite observed declines in LAI, our findings may be consistent with satellite observations of “green-up” following Amazonian droughts once phenological changes in leaf level photosynthetic capacity are accounted for. This study highlights the potential importance of studying drought-induced changes in three-dimensional forest structure and may provide a means to reconcile plot- and satellite-based studies.