Effects of drought on forest structure: Esecaflor, FLONA Caxiuanã

Effects of drought on forest
structure: Esecaflor, FLONA
Caxiuanã
Team: Sky Walker - THE
LEGEND
Diego Brandão
João Athaydes
Juliana Schiett
Lilia Assunção
Scott Stark
Suelen Marostica
Caxiuanã – PA – Brazil
2009
Why study the effect of
drought in the structure of the
forest?
Future scenarios predict more
intense and frequent droughts in
the Amazon.
Effects of dry season in the
forest
•
•
•
•
•
Change in microclimate
Decrease in soil humidity  feedbacks
Increase of litterfall  changes in leaf area
Increase risks of fires  litterfall flammability
Changes in photosynthetic and growth rates
and GPP
More severe and frequent droughts can
intensify these effects  changes in
forest structure
Does forest structure respond to
rainfall exclusion?
H0: rainfall exclusion does not affect the forest
structure
H1: rainfall exclusion affects the forest structure
•
•
•
•
Leaf area index (LAI)
Canopy maximum height
LAI along the forest vertical profile
Energy Transmission and Absorptance
Field site:
- Caxiuanã National
Forest, Para State, northeastern Brazil (1°43’3.5”S,
51°27’36”W);
- Annual rainfall (~ 2,272
mm) and a pronounced
dry season;
- Soil: yellow Oxisol
(Brazilian
classification: Latosol);
- Two plots (Control and
exclusion)
Methodology
10
30
Points of measurement of LAI
50
70
90
N
10
30
50
70
90
Methodology
• Leaf Area Index (LAI) is defined as the amount of
leaf area per unit ground area.
• We estimated LAI at 25 points in Control and DryDown plots with hemispherical photography. We used
a Nikon Coolpix 990 camera with a Nikon fisheye
converter (FC-E9 0.21 x ) and the program Can-eye
(Version 5).
Methodology
LIDAR = Light Detection and Ranging
measured distance from ground to canopy
g LIDAR (2,000 pulses per second) in 5
in Control and Dry-Down plots .
Estimating Leaf Area with LiDAR
Break the canopy into pixels (a grid)
The ratio between the number of
reflections in a pixel (grid cell) and the
number of lidar shots that reach the pixel
is an estimate of Leaf Area
This Leaf Area estimate is the based on
the horizontal projection of the leaves and
is an under estimate
ethodology
hemispherical photo
LAI - Lidar
x
55 m height
x
21 to 55 m height
x
11 to 20 m height
x
1 to 10 m height
Results
1. Hemispherical photo
LAI –hemispherical photo x LAI - Lidar
LAI - hemisferical photo
6
5
4
3
2
1
2
3
4
5
I –hemispherical photo x
I – Lidar in different strata
1 to 10 m
11 to 20 m
21 to 55 m
LAI - hemispherical photo
6
5
4
3
2
0
5
4
3
2
6
LAI - hemispherical photo
LAI - hemisferical photo
6
1
2
LAI - 1 to 10 m height
5
4
3
2
3
Differences in LAI in Control and Seca plot
5
LAI - lidar
4
3
2
ctr
seca
1
ctr
seca
Spatial distribuition of LAI (Hemispherical photos)
Dry
Control
CTR
DRY
CTR
DRY
CTR
DRY
CTR
DRY
Conclusions
here is no agreement between hemispherical and lidar
sed LAI measurements;
his may result from different scales of analysis;
here is no difference between seca and control in leaf
ea or leaf area profiles;
here appears to be less leaf area in the undersory .