**1A: Remote Sensing and the Carbon Cycle**   

**Objective Indicators of Pasture Degradation from Spectral Mixed Model
Analysis of Landsat Imagery**

**Eric A. Davidson**, Woods Hole Research Center, edavidson@whrc.org
(Presenting)\
**Gregory P. Asner**, Carnegie Institute, gpa@stanford.edu\
**Thomas A. Stone**, Woods Hole Research Center, tstone@whrc.org\
**Christopher Neill**, Marine Biological Laboratory, cneill@mbl.edu\
**Ricardo de O. Figueiredo**, Embrapa Amazônia Oriental,
ricardo@cpatu.embrapa.br

Degradation of cattle pastures is a major concern for management and for
understanding the future land cover/land use consequences of ongoing
Amazonian deforestation. Unfortunately, "degradation" is not well
defined and may have different meanings for ranchers, ecologists, and
policy makers. The objective of this study is to quantify pasture
degradation using objective scalars of photosynthetic vegetation (PV),
non-photosynthetic vegetation (NPV), and exposed soil derived from
spectral mixture analyses of Landsat imagery. The amount of exposed soil
and NPV, such as senescent grass foliage, increases as pastures age and
as the grasses become less productive. We employed a general,
probabilistic spectral mixture model (AutoMCU) for decomposing satellite
spectral reflectance measurements into sub-pixel estimates of PV, NPV,
and bare soil covers at Fazenda Nova Vida in Rondonia and Fazenda
Vitoria in Pará. These two ranches vary by size, age, soils, and
management practices. The Nova Vida ranch had higher stocking densities,
was more intensively managed, and had larger values of estimated exposed
soil than did Fazenda Vitoria. The number of management "treatments" at
Nova Vida, which included liming, herbiciding, and disking, was weakly,
but significantly positively correlated with exposed soil and negatively
correlated with PV across pasture management units. At both ranches, PV
and NPV were strongly negatively correlated, and PV values were
generally lower at the more intensively managed Nova Vida ranch.
Although this analysis demonstrates that Nova Vida ranch shows signs of
pasture degradation as defined by these objective criteria, it
nevertheless has been maintained as a highly productive pasture system
through intensive management and relatively high inputs. This remote
sensing technique successfully reveals variation in objectively defined
degradation indicators between and within ranches, but these degradation
indicators do not necessarily imply reduced current cattle production.

**Submitted by** Eric Davidson

**Abstract ID:** 68

 

**Modeling Selective Logging and Carbon Cycling in the Brazilian
Amazon**

**Greg Asner**, Carnegie Institution, gpa@stanford.edu (Presenting)\
**Maoyi Huang**, Carnegie Institution,
mhuang@globalecology.stanford.edu\
**David Knapp**, Carnegie Institution, deknapp@stanford.edu

A new three dimensional version of the Carnegie-Ames-Stanford Approach
(CASA) ecosystem model (CASA-3D) was developed to simulate the carbon
cycling in tropical forest ecosystems after disturbances such as
logging. CASA-3D has the following new features: (1) an alternative
approach for calculating absorbed photosynthetically-active radiation
(APAR) using new high-resolution satellite images of forest canopy gap
fraction; (2) a pulse disturbance module to modify aboveground carbon
pools following forest disturbance; (3) a regrowth module that simulates
changes in community composition by considering gap-phase regeneration;
and (4) a radiative transfer module to simulate the dynamic
three-dimensional light environment above the canopy and within gaps
after forest disturbance. The model was calibrated with and tested
against field observations from experimental logging plots in the
Large-scale Biosphere Atmosphere Experiment in Amazonia (LBA) project,
and the sensitivity of key model parameters were evaluated with Monte
Carlo simulations, based on which the uncertainties of simulated NPP and
respiration associated with model parameters and the meteorological
variables were assessed. We found that selective logging causes changes
in forest architecture and composition that result in a cascading set of
impacts on the carbon cycling of rainforest ecosystems. Our model
sensitivity and uncertainty analyses also highlight the paramount
importance of measuring changes in canopy gap fraction from satellite
data, as well as canopy light-use efficiency from ecophysiological
measurements, to understand the role of forest disturbance on landscape
and regional carbon cycling in tropical forests. Finally, our study
suggests that CASA-3D may be suitable for regional-scale applications to
assess the large-scale effects of selective logging, to provide guidance
for forest management, and to understand the role of forest disturbance
in regional and global climate studies.

**Submitted by** Gregory Asner

**Abstract ID:** 12

 

**Amazon Forests Green-up during 2005 drought**

**Scott R. Saleska**, University of Arizona, saleska@email.arizona.edu
(Presenting)\
**Kamel Didan**, University of Arizona, kamel@Ag.arizona.edu\
**Alfredo R. Huete**, University of Arizona, ahuete@Ag.arizona.edu\
**Brad Christoffersen**, University of Arizona,
bchristo@email.arizona.edu\
**Natalia Restrepo-Coupe**, University of Arizona,
ncoupe@email.arizona.edu

Coupled climate-carbon cycle modeling studies indicate that Amazon
forests are vulnerable to drought, and some predict substantial carbon
loss from tropical ecosystems, including the drought-induced collapse of
the Amazon forest and conversion to savanna. The model-simulated future
forest collapse is attributable, in part, to a forest physiological
feedback mechanism which should be observable as reductions in
transpiration and photosynthesis during drought years under current
climates.\
\
\
\
A widespread drought occurred in the Amazon in 2005, the first such
climatic anomaly since the launch of the Terra satellite's MODIS sensor
in 1999, providing a unique opportunity to compare actual forest drought
response to expectation on broad spatial scales. Contrary to expectation
based on model simulations, satellite observations showed a large-scale
green-up in intact evergreen forests of the Amazon in response to the
2005 drought. These findings suggest that Amazon forests, though
threatened by human-caused deforestation and fire, may be more resilient
to climate changes than ecosystem models assume.

**Submitted by** Scott Saleska

**Abstract ID:** 98

 

**MODIS vegetation indices for detecting the 2005 Amazon drought**

**Liana O. Anderson**, Oxford University Centre for the Environment,,
lander@ouce.ox.ac.uk (Presenting)\
**Yadvinder Malhi**, Oxford University Centre for the Environment,,
ymalhi@ouce.ox.ac.uk\
**Luiz E.O.C. Aragao**, Oxford University Centre for the Environment,,
laragao@ouce.ox.ac.uk\
**Sassan Saatchi**, Jet Propulsion Lab National Aeronautics and Space
Administration, saatchi@congo.jpl.nasa.gov

In the last decades, the detection of drought occurrences and assessment
of its severity using satellite data are becoming popular in disaster,
desertification, crop production, phenology, land cover change and
climate change studies. To detect the drought effects on different
vegetation types, many methodologies have been developed, mostly relying
on the use of vegetation indices. This communication reports the first
attempt to assess the capability of MODIS NDVI, Enhanced Vegetation
Index (EVI) and Normalized Difference Water Index (NDWI) time-series to
detect the spatial pattern of the 2005 drought in Amazonia. To reach
this objective, monthly composites of the MOD13A2 product were generated
for the 2000 to 2006, based on maximum NDVI pixel value, for the entire
basin. A South American land cover map updated with deforestation until
2005 for the Brazilian Amazon associated with a rainfall anomaly derived
from TRMM data were used as basis for the sampling scheme. To identify
intensity and duration of the canopy change / stress due to the drought
across Amazonia, we calculated vegetation indices anomalies for 2005 and
2006 (NDVIanomaly, EVIanomaly, NDWIanomaly) as the departure from the
2000 - 2006 mean (VI2000 - 2006), normalized by the standard deviation
(σ) in a pixel-by-pixel basis, based on 5 samples in 3 distinct areas
affected by the rainfall anomaly in 2005. The spatial distribution
analyses were based on re-sampling data to 0.25 degrees to diminish
cloud coverage and noise in the dataset. Then, vegetation indices
anomalies were calculated. To support the data interpretation, literfall
data for 2 sites (Southern Colombia and Eastern Brazil) from 2004 to
2006 were used. Our preliminary results showed that despite the high
variability in the vegetation indices response in the temporal series,
they detected a persistence of an anomalous signal during 2005/2006. The
spatial analysis showed NDWI anomaly in Jun/Jul 2005 in a region that is
not used to water deficit, suggesting that this areas can be more
sensitive to drought events and climate change. Finally, for the first
site evaluated (Colombia), vegetation indices seems to not reflect
literfall variability, suggesting that shade and other factors might be
affecting vegetation indices response.

**Submitted by** Liana Anderson

**Abstract ID:** 55

 

**Severe storms and blow-down disturbances in the Amazon forest**

**Fernando Del Bon Espirito-Santo**, Institute for the Study of Earth,
Oceans and Space (EOS), Complex System Research Center (CSRC), Morse
Hall, University of New Hampshire, Durham NH., fernando@guero.sr.unh.edu
(Presenting)\
**Michael Keller**, Institute for the Study of Earth, Oceans and Space
(EOS), Complex System Research Center (CSRC), Morse Hall, University of
New Hampshire, Durham NH.,, michael.keller@unh.edu\
**Robby Braswell**, Institute for the Study of Earth, Oceans and Space
(EOS), Complex System Research Center (CSRC), Morse Hall, University of
New Hampshire, Durham NH., rob.braswell@unh.edu\
**Gilberto Vincente**, NOAA, Product Implementation Branch, Camp
Springs, MD., gilberto.vicente@noaa.gov\
**Steve Frolking**, Institute for the Study of Earth, Oceans and Space
(EOS), Complex System Research Center (CSRC), Morse Hall, University of
New Hampshire, Durham NH., steve.frolking@unh.edu

Large (area ≥ 1 ha) natural disturbances in old-growth tropical forests
are caused by a variety of processes such as landslides, fires, wind,
and cyclonic storms. We analyzed the pattern of large forest
disturbances apparently caused by severe winds (blow-downs) in a mostly
unmanaged portion of the Brazilian Amazon using a longitudinal transect
of Landsat images (27 scenes) between Lat/long 6º43'W 68º50'S and 2º16'W
51º51'S, respectively and daily precipitation estimates based on NOAA
satellite data. We found 170 blow-downs with an average area of 3 km2.
Most blow-down disturbances occurred in Western Amazon between
longitudes 67º and 58º W. A map of heavy rainfall ( ≥ 20 mm d-1) showed
that the maximum frequency of heavy daily rainfall (\~80 days y-1)
occurred around at the longitude 63º in our study region. We found a
close relation between the frequency of heavy storms and the occurrence
of blow-down disturbances events. This result suggests a close relation
between severe weather and the rate of forest turnover caused by
blow-down disturbances. The forest turnover time calculated for these
disturbances in 9 Eastern Landsat scenes studied was almost 9000 years
whereas for the 18 scences in the Western Amazon, turnover time was
closer to only 1200 year. Large disturbance may have a significant
influence on spatial pattern of forest dynamics and productivity of the
Amazon.

**Submitted by** Fernando Espírito-Santo

**Abstract ID:** 33