PE&RS October 2015 - page 807

Marsh Canopy Leaf Area and Orientation
Calculated for Improved Marsh Structure Mapping
Elijah Ramsey III, Amina Rangoonwala, Cathleen E. Jones, and Terri Bannister
An approach is presented for producing the spatiotemporal esti-
mation of leaf area index (
) of a highly heterogeneous coastal
marsh without reliance on user estimates of marsh leaf-stem
orientation. The canopy
profile derivation used three years of
field measured photosynthetically active radiation (
) vertical
profiles at seven S. alterniflora marsh sites and iterative trans-
form of those
attenuation profiles to best-fit light extinction
coefficients (
sun zenith dependency was removed obtain-
ing the leaf angle distribution (
) representing the average
marsh orientation and the
used to calculate the
reproduced measured
profiles with 99
percent accuracy and corresponded to field documented struc-
better reflect marsh structure and results sub-
stantiate the need to account for marsh orientation. The structure
indexes are directly amenable to remote sensing spatiotemporal
mapping and offer a more meaningful representation of wetland
systems promoting biophysical function understanding.
Marshes occupy a unique interface between water bodies
and upland landscapes. They are critical and essential for
exchange of land to water fluxes, for providing habitat, and in
coastal settings, for the protection of people and facilities. In
the coastal interface, they experience high frequency shifts in
inundation modulated by terrestrial runoff and coastal ocean
tides and storms. These dynamic modulations create a spatial-
ly complex landscape exhibiting various wetland communities
and canopy structure forms, even within monotypic marshes
within the same region. In addition, the way these wetlands
are managed can have substantial impacts on the biophysical
and compositional structure of the marshes and the animal
communities dependent on these habitats (Middleton, 1999).
In order to track the health and function of these coastal
marshes the structural characteristics must be documented
frequently, on the order of bimonthly to at least seasonally.
The necessity in spatiotemporal tracking of marsh struc-
ture, and the vertical profile, is based on the interdependence
of the marsh structure and the coastal zone biophysical func-
tion. Biophysical values such as the leaf area index (
) have
proved useful in a number of environmental applications to
represent biomass density. The three-dimensional spatial vari-
ability of
in grassland is needed to estimate key compo-
nents in biochemical cycles (Lantinga
et al.
, 1999; Rakocevic
et al.
, 2000). These include surface water balance and produc-
tivity (and photosynthesis) (Anwar
et al.
, 2012; Juarez
et al.
2009; Mitchell
et al.
, 1998; Kappas and Propastin, 2012), and
ozone and CO
assimilation (Jäggi
et al.
, 2006; Kappas and
Propastin, 2012; Lantinga
et al.
, 1999). In addition, the three-
dimensional canopy description is useful for improving water
flow estimates, advancing optical condition and change map-
ping, and fire burn dynamics and emission projections.
an essential factor in climate, weather, and ecological studies,
and thus, a factor in global climate (Juarez
et al.
, 2009; Kappas
and Propastin, 2012).
Optical Land Cover Mapping and Canopy Architecture
The incorporation of canopy structure in calibration advances
remote sensing mapping of subtle changes as well as bulk
canopy changes. Multiple canopy contributors such as plant
cover percent and background variability (Colwell, 1974;
Chance and Cantu, 1975; Hardisky
et al.
, 1983; Myneni
et al.
1995) complicate linking the leaf reflectance to canopy reflec-
tance. Even accounting for these landscape and background
influences, the canopy reflectance as represented in the
remote sensing signal reflects the intertwined contributions
of the leaf optical properties and plant-canopy structure (i.e.,
density, orientation) (Allen and Richardson, 1968; Ranson
et al.
, 1985; Huete and Jackson, 1988; Peterson
et al.
, 1988;
et al.
, 1993; Lorenzen and Jensen, 1988; Penuelas and
Filella, 1998; Spanglet
et al.
, 1998).
In cases where the target canopies exhibit largely “full and
uniform coverage” or the leaf and structure are considered a
combined unit such as in land cover classifications, the decou-
pling of the leaf optical and plant-canopy structure contribu-
tions to the canopy reflectance may be of little consequence.
Where the objective of the optical mapping is to capture subtle
change such as decreasing leaf pigments accompanying the de-
cline of canopy condition, however, the removal of the canopy
structure and background contribution to the canopy reflec-
tance may be advantageous or even critical to detecting desired
changes. This latter scenario applies to the detection of change
in the canopy reflectance brought about by the addition of oil
or other optically modifying substances into the marsh canopy.
Standardization of Canopy Architecture
The transforms applied to the remote sensing data in order
to estimate biomass as one component of canopy structure
include inferential relationships, vegetation indexes, and
assorted mixture of specialized methods. Many of these are
based on vegetation indexes (
) adapted from the concept of
Tucker (1979) that has proved extremely useful in estimating
biomass in vegetated landscapes from forests to grasslands.
However, the widespread adjustment through adaption of
index form or variable input in order to enhance performance
specificity to target vegetation biomass has led to a plethora
Elijah Ramsey III and Amina Rangoonwala are with the US
Geological Survey, Wetland and Aquatic Research Center, 700
Cajundome Blvd., Lafayette, LA 70506 (
Cathleen E. Jones is with the Jet Propulsion Laboratory,
California Institute of Technology, M/S 300-319 4800 Oak
Grove Drive, Pasadena, CA 9110.
Terri Bannister is with the Regional Application Center,
University of Louisiana at Lafayette, 104 East University
Avenue, Lafayette, LA 70504.
Photogrammetric Engineering & Remote Sensing
Vol. 81, No. 10, October 2015, pp. 807–816.
© 2015 American Society for Photogrammetry
and Remote Sensing
doi: 10.14358/PERS.81.10.807
October 2015
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