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Collaborative Research: Understanding controls on the phenology of tropical vegetation
photosynthesis: from leaf traits to landscapes
The phenology of tropical forests is a fundamental manifestation of the ecology and
evolutionary biology of evergreen systems, and accurate seasonal measurements of the
components of forest metabolism provide a basic first-order test of our understanding of these
critically important systems. Much attention has focused in recent years on model predictions
tropical forest dieback under future climate change, but model skill at predicting even present-day
seasonality of tropical forest photosynthesis is poor, implying that better understanding of
mechanisms underlying tropical phenology is needed.
Remote sensing methods directly detect photosynthetic pigments in vegetation, and could be
a powerful tool for investigating spatial and temporal patterns of photosynthetic metabolism in
challenging tropical environments, but uncertainty remains about atmospheric artifacts from highly
seasonal cloud cover and aerosol loads in tropical atmospheres.
We propose to investigate these questions, testing mechanistic hypotheses about controls
on the seasonality of photosynthesis from individual leaf to whole canopy, as well as the
methodological null hypothesis that remotely detected seasonal patterns (e.g. reports of dry season
green-up) are a consequence of atmospheric aerosol contamination of the surface reflectance,
rather than a true vegetation response. We seek to design tests capable of rigorously rejecting this
null hypothesis, an essential prerequisite for remote sensing methods to be used as a credible
scaling tool for making large-scale inferences about responses to climatic variability and change.
Intellectual Merit: The research proposed here would address this important problem by
integrating in situ and remote sensing measurements at three sites: two different primary forest sites
in the Amazon (near Santarem and, leveraging participation of Brazilian-funded collaborators, near
Manaus), and one agricultural site (near the Santarem forest site). We propose three components:
(1) LOCAL SCALE measurements (from the ground) of the phenology of (a) root, leaf and canopy
ecophysiology (from individual leaves accessed by tree climbers to whole-canopy hyperspectral
imaging and eddy flux measurements), of (b) atmospheric characteristics (including aerosol depth
and cloud cover), and of (c) surface radiation, including the angular distribution of direct and diffuse
components of PAR (using a sophisticated CMOS for radiometric hemispheric imaging).
(2) LARGE SCALE measurements (from space, using MODIS and hyperspectral images on
Hyperion) of the phenology of remotely sensed spectral indices of vegetation function.
(3) MODELING. Using the first airborne LIDAR dataset obtained for the Amazon and measurements
from (1), we will parameterize models of 3-D canopy photosynthesis. The goal is to scale up our
integrated understanding of vegetation characteristics (including leaf spectral reflectance), radiation
components (including aerosol-, cloud-, and subcanopy-influenced effects of diffuse radiation
fraction and angular distribution), and the seasonality thereof.
Broader Impacts: This small proposal would leverage a broad scientific impact by producing a highquality dataset of the phenological rhythms of forests in the Amazon system at multiple scales. Such
data are a necessary prerequisite to understanding the critical question of Amazonian forest
response to climate variation and change. This proposal also takes substantial advantage of
already-funded or ongoing projects (principally an NSF-funded Partnership for International
Research and Education), making it a highly economical use of research funds.
The proposed project will impact society through important policy-relevant science relating to
carbon cycling and climate change; support of international collaboration between U.S. and Brazilian
partners, leveraging significant added value from Brazilian government support; participation by
undergraduate and graduate students in an intensive annual 2-week field course in the Amazon, to
which this project would bring a “remote sensing and phenology” component; hands-on training in
use of remote sensing cameras in the tropical forest biome of Biosphere 2, and participation by
students there in outreach to the U.S. public; and potential recruitment of a female or minority postdoc for the project, leveraging the University of Arizona’s strong minority recruitment program and its
traditionally strong Hispanic and Native American enrollments.