Directional Reflectance Distrbutions of a Hardwood and Pine Forest

281
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, VOL. GE-24, NO. 2, MARCH 1986
Directional Reflectance Distrbutions of a Hardwood
and Pine Forest Canopy
DANIEL S. KIMES, W. WAYNE NEWCOMB, ROSS F. NELSON,
AND
JOHN B. SCHUTT
T HE COMPLEX radiant energy interactions that take
place in various Earth surface scenes must be understood for future advancement in remote-sensing technology. Forests represent an important component of the
Earth's surface that have a unique heterogeneous structure
relative to homogeneous grasslands and agricultural crops.
Few directional measurements of forest canopies have
been reported in the literature [1]-[3]. The only study to
report the directional reflectance of a forest canopy over
the entire exitance hemisphere as a function of solar zenith angle was Kriebal [4]. Furthermore, no studies have
physically analyzed the unique radiant transfers that take
place in forest canopies. Understanding the scattering behavior of forest canopies is of importance to both regional
and global remote-sensing studies.
In this study, the directional reflectance distributions in
AVHRR bands 1 (0.58-0.68 ,um) and 2 (0.73-1.1 ,um)
were measured as a function of sun angle for both a hardwood and pine forest canopy at Beltsville, Maryland, in
June from a helicopter platform. The reflectance distributions are reported and compared to the scattering behavior of agricultural and natural grassland canopies as
measured and modeled by Kimes et al. [5]-[8]. In addition, we used the unique radiative transfer model of Kimes
[6], [9] to extend our understanding of the physical principles causing the scattering behavior in forest canopies.
Only a few physical analyses have been performed to
quantitatively understand the physical mechanisms that
cause the observed dynamics of directional reflectance
distributions as a function of solar zenith angle, geometric
structure (leaf orientation and plant spacing), and leaf and
soil spectral properties [6], [7]. No such studies have been
performed for forest canopies. Most radiative transfer
models of vegetation assume infinitely extended horizontal layers of spatially homogeneous vegetation components over a Lambertian soil as reviewed by Smith [1],
[2]. Several of these models have been modified to treat
heterogeneous row crops as reviewed by Smith [1], [2].
There are several geometric optics models that have been
developed for a single type of heteorogeneous scene. For
example, Strahler and Li [10] modeled forest canopies
using cones and Jackson et al. [11] used rectangular solids to model row crops. Other similar models are reviewed by Smith [1], [2]. In contrast, two models have
been developed that have the capability of modeling radiant transfers in any 3-D heteorgeneous scenes of vegetation canopies (e.g., row crops, orchards, open forest,
etc.) [6], [12]. The 3-D radiant transfer model of Kimes
[6], [9] was applied to document the unique radiant transfers that take place in forest canopies due to their special
geometric structure.
Manuscript received April 3, 1985; revised September 13, 1985.
D. S. Kimes, R. F. Nelson, and J. B. Schutt are with the Earth Resources Branch, NASA Goddard Space Flight Center; Greenbelt, MD
20771.
W. W. Newcomb is with Republic Management Systems, Inc., Applications Project, Landover, MD 20785.
IEEE Log Number 8407043.
II. EXPERIMENT
A. Radiometric Measurements
All field data were collected at the Beltsville Agricultural Research Center, U.S.D.A., in June 1984 (Table I).
All spectral radiance measurements were taken from a
Abstract-The directional reflectance distributions for both a hardwood and pine forest canopy at Beltsville, Maryland, were measured
in June as a function of sun angle from a helicopter platform using a
hand-held radiometer with AVHRR band 1 (0.58-0.68 Mm) and band
2 (0.73-1.1 Mm). Canopy characteristics were measured on the ground.
The reflectance distributions are reported and compared to the scattering behavior of agricultural and natural grassland canopies. In addition, the three-dimensional radiative transfer model of Kimes was
used to document the unique radiant transfers that take place in forest
canopies due to their special geometric structure.
Measurements and model simulations showed that the scattering behavior of relatively dense forest canopies is similar to the scattering
behavior of agricultural crops and natural grasslands. Only in more
sparse forest canopies with significant spacing between the tree crowns
(or clumps of tree crowns) does the scattering behavior deviate from
homogeneous agricultural and natural grassland canopies. This clumping of vegetation material has two effects on the radiant transfers within
the canopy: A) it increases the probability of gap to the understory
and/or soil layers that increases the influence of the scattering properties of these lower layers; and B) it increases the number of low
transmitting clumps of vegetation within the scene causing increased
backscatter and decreased forward scatter to occur relative to the homogeneous case. Both effects, referred to as phenomenon A and B,
respectively, tend to increase backscatter relative to forward scatter.
For typical forest canopies, the peak backscatter reflectance can be
increased as much as 30 percent relative to an equivalent homogeneous
canopy due to phenomenon A and 35 percent due to phenomenon B.
The combined effect of phenomenon A and B can cause typical increases of 65 percent or higher. It is hypothesized that these phenomena are especially important in sparse conifer forests, such as boreal
forests, that account for 50 percent of the world's forest area.
I. INTRODUCTION
U. S. Government work not protected by U.S. copyright
282
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, VOL. GE-24, NO. 2, MARCH 1986
TABLE I
FOREST CANOPY MEASUREMENT TIMES AND CORRESPONDING SOLAR ZENITH
ANGLES
CANOPY
DATE
EASTERN
STANDARD
TIME
SOLAR ZENITH
ANGLE
Hardwood
6/1/84
5:50 a.m.
790
6/7/84
7:15 a.m.
630
Pine
8:50 a.m.
450
10:50 a.m.
250
6/1/84
6:20 a.m.
740
6/7/84
7:35 a.m.
590
9:10 a.m.
410
10:50 a.m.
230
Xz. SENSOR
=00
(a)
-J
z
LLi
0.
Bell-Jet Ranger helicopter at approximately 90-m altitude
above the top of the canopy. Spectral directional radiances were taken in NOAA satellite 6-9 AVHRR bands
1 (0.58-0.68 ,um) and 2 (0.73-1.1 ,um) using a Mark-III
three-band radiometer with a restricted 120 field of view.
The circular footprint of the nadir looking radiometer at
the top of the canopy had a diameter of 19 m, assuming
a mean altitude of 90 m. For each measurement period,
41 directions were measured located at nadir and at 150
increments of off-nadir angle (15°, 300, 450, 600, and
750) and 450 increments of azimuth angle (00, 450, 900,
1350, 1800, 2250, 2700, and 3150). The 00 azimuth corresponds to the direction of the sensor looking toward the
sun. Thus, an azimuth of 00 and 1800 represents forward
scattering and backscattering, respectively. The coordinate system used is shown in Fig. 1.
For each measurement period, two or three complete
directional radiance distributions were taken at different
sampling points within the middle of a homogeneous surface. This sampling procedure took less than 20 min. All
directional radiance values were divided by the corrected
radiance from a horizontal barium sulfate panel. The resulting values are reflectance factors [13]. The corrected
radiance from a barium sulfate panel refers to corrections
made for the non-Lambertian behavior of the reference
panel for the specific irradiance conditions as described
by Kimes and Kirchner [13]. For these corrections, the
distribution of diffuse sky radiance was taken from the
simulated data sets of Dave [14]. One mean distribution
was calculated for each measurement period. All of the
reflectance distributions were essentially symmetric about
the principle plane of the sun. Therefore, corresponding
data points on either side of the principle plane of the sun
(Fig. 1) were averaged (e.g. azimuths 45° and 315°, 90°
and 2700, and 1350 and 2250 and averaged for equal offnadir view angles). The coefficient of variation of reflectance for the various view directions and wavelength
bands was on the order of 0.4. This statistic is reported
to give the reader a feel for data dispersion. Keep in mind,
0
(b)
Fig. 1. (a) Coordinate system defining solar and sensor angles and (b) polar plot showing scheme for plotting directional reflectance factors. The
solar azimuth is always 1800. The sensors azimuth and off-nadir angles
are shown as 1 and 0, respectively. A sensor with a 00 azimuth looks
into the sun. Thus, an azimuth of 0° and 1800 represents forward scattering and backscattering, respectively. The spectral directional reflectance factors were plotted in a polar plot, where the distance from the
origin represents the off-nadir view angle of the sensor and the angle
from 0 = 00 represents the sensor's azimuth. The points show the directional measurements plotted. Lines of equal percent reflectance were
contoured as presented in Fig. 7. Only 0- 180° azimuth is shown because
azimuthal symmetry about the principle plane of the sun is assumed. The
principle plane is defined as the plane perpendicular to the horizontal
ground and contains the solar azimuth.
however, that in many cases only four sample points were
taken for each view direction.
The method of plotting the directional reflectance factor
distributions is described in Fig. 1. The radiometric data
were collected for various solar zenith angles as reported
in Table I.
B. Site Description
Ground data were collected in the hardwood and pine
forest sites as shown in Fig. 2 to provide a general description of each site. Sample plots were selected by transect at each site. Ten plots were chosen in the larger hardwood tract; eight plots were chosen in the pine tract.
Circular plots incorporating 0.0263 ha (9.14-m radius)
were located in the hardwood tract. Due to significantly
higher stem counts encountered on the pine site, two different plot sizes were used in order to reduce the number
of trees tallied. Plots of 0.01 17 ha (6. 10-m radius) were
used when 10 or more trees could be tallied within that
sample area; plots of 0.0263 ha were used on less dense
samples.
Within each sample plot, the diameter at breast height
(dbh) of all trees greater than 10.2 cm (4 in) were mea-
283
KIMES et al.: DIRECTIONAL REFLECTANCE DISTRIBUTIONS
TABLE II
CHARACTERISTICS OF THE HARDWOOD AND PINE TRACTS
(The mean plus or minus one standard deviation is given where
appropriate.)
Hardwood Tract
Predominate
Tree Species
Basal Area +
(m2/hectare)
Number of Stems'
(Stems/hectare)
Average Height
(m)
26 ± 12
18 t 9
377 ± 198
22
±
6
987
480
±
11 ± 4
8
93
92
7
by Stem Count
% Conifer
8
93
% hardwood
92
7
*
Basal area is the area of the plane passed through the stem of the tree at
right angles to the longitudinal axis of the tree. The sum of these
wooden cross sectional areas per unit land area is given (in meter2 per
hectare).
These numvbers denote counts of stems with diameters greater than
to 10.16 cm (4") at breast height.
or
equal
available to them [15]. The hardwood tract is over
90 percent hardwood species based on basal area and stem
counts. The Virginia pine is found for the most part on
the periphery of the hardwood tract on the drier soils surrounding the bottomlands.
The pine tract is significantly younger and smaller (in
terms of basal area and height) than the hardwood tract.
Though the trees on this pine tract are physically smaller,
the stand is, like the hardwood tract, fully stocked. The
number of stems found per unit area are over 2.5 times
the counts found in the hardwood forest. Height uniformity, the purity of the stands found throughout this sandy,
well-drained pine tract, and proximity to on-going agricultural activity suggest old field succession of Virginia
pine. Other species include shortleaf pine (Pinus enchinata Mill.), sweet gum, northern red oak, and red maple.
The pine tract is over 90-percent softwood species; 83
percent of those softwoods are Virginia pine, the remainder is shortleaf pine.
Other important characteristics of each forest canopy
were measured. At each sample site, the probability of
gap (PGAP) through the forest canopy to the ground was
measured as a function of off-nadir view angle (0). It was
assumed that this function was constant with azimuthal
orientation. Color photographs were taken at each view
direction. The center portion of each photograph was
projected onto a dot grid consisting of 200 dots. The number of dots laying on a vegetation component or a gap
were tallied. The mean proportion of gap for each offnadir view direction was calculated for all sample points
of each forest canopy. The bound on the errors of estimation of the proportion of gap (g) can be calculated as
two times the square root of the estimated variance of g.
The estimated variance is calculated as
space
sured and the tree species were recorded. The average
height estimate was obtained for each sample plot
Suunto Hypsometer. These data were used to estimate basal area per hectare, percent conifer/hardwood
within the tract, average height, and number of stems per
hectare. The results were reported in Table II.
The hardwood tract includes lowland and upland sites,
each with characteristic eastern U.S. hardwood species
mixes. The lowland species growing on a poorly drained
floodplain are predominatly red maple (Acer rubrum L)
and black gum (Nyssa sylvatica Marsh). Other lowland
species include swamp chestnut oak (Quercus prinus L.),
American hornbeam (Carpinus caroliniana Walt.), green
ash (Fraxinus pennsylvania var lanceolata (Berkh) Sarg.),
holly (llex opaca Ait.), and willow oak (Quercus phellos
L.). The well-drained areas adjacent to the floodplain support American beech (Fagus grandifolia Ehrh.) black
gum, sweet gum (Liquidamber styraciflua L.), and tulip
poplar (Liriodendron tulipifera L.). Other upland species
include nothern red oak (Quercus rubra L.), white oak
(Quercus alba L.), red maple, and Virginia pine (Pinus
virginiana Mill.). The data in Table II describe fully
stocked stands occupying over 95 percent of the growing
Virginia pine
% hardwood
+
canopy
using a
Lowland: red maple. blackgum
Upland: american beech,
tulip popular
Stand Compositi on:
by Basal Area
t Conifer
(a)
(b)
Fig. 2. Aerial photographs of the (a) pine and (b) hardwood forests in
Beltsville, MD, on June 1984.
Pine Tract
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, VOL. GE-24, NO. 2, MARCH 1986
284
1.0
TABLE III
PROBABILITY OF GAP (p.ap(G)) THROUGH THE PINE AND HARDWOOD FOREST
CANOPIES TO THE GROUND AS A FUNCTION OF OFF NADIR VIEW ANGLE (0)
.8
c)
OFF-NADIR ANGLE (8)
0°
300
15°
450
600
z
750
Pine Forest
.25
.24
.23
.14
.096
.018
Hardwood Forest
.21
.15
.14
.14
.082
.026
.6
0
.4
gq N-n
(n-1) N
where q is the estimated proportion of vegetation material, n is the number of dots sampled, and N is the population size. For this specific case (N - n)/N is considered
to be 1.0 [16]. Thus, in this study the maximum bound
on the error of estimation for any photograph would be
+0.071 for a 50-percent probability of gap. The results
are shown in Table III.
Using a plumb-bobed protractor as described by Ranson et al. [17], the leaf inclination of 1000 leaves were
measured on four isolated trees of the major hardwood
species: red maple, black gum, American beech, and tulip
popular. Each tree was under four meters in height and
occurred on the edge of the forest clearing. The mean frequency distribution of leaf inclination is shown in Fig. 3.
Azimuthal symmetry in leaf orientation was assumed in
this study.
Hemispherical leaf reflectance and transmittance in
AVHRR bands 1 and 2 were sampled using a Beckman
DK2 Spectroradiometer for the four major hardwood species and Virginia pine. The leaf optical properties for each
species is presented in Table IV. The mean reflectance
and transmittance values for the four hardwood species
was 0.056 and 0.051, respectively, for band 1 and 0.46
and 0. 50, respectively, for band 2. These values were used
in model simulations.
C. Model Simulations
The upgraded three-dimensional radiative transfer
model of Kimes [6] was used to explore some unique radiative properties of forest canopies. The upgraded anisotropic soil algorithm was used in this study. Only a
brief description of the model is presented here as a more
detailed description may be found in [6], [7], [9]. The
conceptual framework of the model is a retangular solid
of any dimension that is subdivided into cubical cells of
unit dimensions. Individual cells are identified by their x,
y, and z coordinates (Fig. 4). Each cell is associated with
information about the elements (e.g., leaves) within the
cell. This information is used to define the manner in
which the elements interact with radiation. For example,
in the case of vegetation canopies, each cell has specific
information about the elements constituting the scene
(leaves, stems, reproductive structures, and soil), expressed as the element-area indexes, the angular distribution of the elements, their spatial dispersion, and optical properties. The information content of a cell can apply
.2
.0
0°
15'
45'
300
75'
60'
90'
LEAF INCLINATION ANGLE
Fig. 3. Mean cumulative frequency distribution of leaf inclination distributions of the hardwood forest canopy (curve a), compared with the classical distributions of an erectophile (curve b), planophile (curve c), and
spherical (curve d) canopies.
Z
y '--CELL (1, 1, 1)
L:x
Fig. 4. Three-dimensional framework of the model showing the rectangular cell matrix, cell coordinate system, diffuse solar sources (only one
is illustrated), and the direct solar source. The diffuse and direct solar
sources are extended down to the surface of each cell on the top surface
of the cell matrix.
TABLE IV
HEMISPHERICAL REFLECTANCE AND TRANSMITTANCE OF LEAVES IN AVHRR
BANDS 1 AND 2 FOR THE MAJOR SPECIES FOUND WITHIN THE HARDWOOD
AND PINE FOREST CANOPIES.
AVHRR BAND 2
- 1.1 pm)
AVHRR BAND 1
(0.58
Red Maple
-
0.68 vm)
(0.73
Reflectance
Transmittance
Reflectance
Transmittance
0.052
0.045
0.47
0.50
Black Gum
0.051
0.068
0.44
0.52
American Beech
0.058
0.051
0.43
0.50
Tulip Popular
D.U64
0.061
0.U39
0.48
0.49
--
--
*Viryinia
Pine
*A mask of needles was used to measure reflectance.
to any object, whether it is man-made or plant-canopy
element. The spatial variation of cell contents among the
cells determines the nature of the 3-D scene as shown in
Fig. 5.
KIMES et al.: DIRECTIONAL REFLECTANCE DISTRIBUTIONS
285
CORRESPONDING
MODULE
ENTIRE SCENE
44%
CROWN
COVERAGE
25%
CROWN
COVERAGE
II
gmP
ROW
CROP
&M
(B)
(A)
79%
CROWN
59%
CROWN
COVERAGE
COVERAGE
(D)
(C)
ORCHARD
4
TOTAL
HOMOGENEOUS
SCENE
2
(J)
-
FOREST
10
URBAN
Fig. 5. Four
scenes
and their cooresponding modules.
Within this framework, all radiant flux is transferred in
defined by the
divided into a
number of contiguous sectors defined by an azimuth (q)
and off-nadir (0) interval. The midvectors of all contiguous sectors in the 47r-sr region define the possible directions of radiant flux sources. These midvectors also define
all possible orientations of leaf normals. The direction and
the magnitude of the flux within each sector are defined
by the midvector.
The diffuse and direct solar sources are extended down
to the target (Fig. 4) and then radiant transfer and scattering processes are simulated within and between the individual cells. The model has been recently upgraded by
calculating a realistic anisotropic phase function of leaforientation distribution (both azimuth and zenith angle
modes), leaf optical properties, and source direction [7].
(The general concept of a phase function is defined by
Chandrasekar [18].) The phase function of a canopy is a
very important component in controlling the scattering
behavior of a vegetated surface and may be defined as the
anistropic scattering that takes place at all interaction
points throughout the canopy. The model may also sima finite number of discrete directions as
user. The spherical coordinate system is
Fig. 6. Nadir view of various forest simulation patterns. Six modules [8]
having 9 x 9 cells each are shown for each pattern. Shaded areas represent cells containing tree vegetation in layers 2-6. The bottom-most
layer of cells (layer 1) had homogeneous understory vegetation in all
cells and is not shown in this representation. The patterns range from a
sparse forest canopy (a) to a homogeneous canopy (e).
ulate the non-Lambertian scattering by the soil [6] using
an algorithm developed by Walthall et al. [19]. Multiple
directional scattering between the cells is then simulated
interactively until all the flux is absorbed, escaped from
the canopy, or reaches a zero threshold. The model is
unique in that it can predict 1) the hemispherical reflectance of the scene, 2) the directional spectral reflectance
factors of a three-dimensional scene as a function of the
sensor's azimuth and zenith angles and the sensor's position above the canopy, and 3) the directional spectral
radiance as a function of the location of a sensor placed
anywhere within the scene. The model was further upgraded by treating radiant transfers in zenith and azimuth
intervals of 100 and 30°, respectively, for a total of 158
possible source directions in 4-r sr as opposed to only 74
source directions in past studies.
A number of simulations were performed to try to show
the unique radiative properties of forest canopies as opposed to homogeneous agricultural crops and natural
grasslands as studied by Kimes [5]-[8]. Band 1 was
treated in this study. Both dense and sparse forest canopies were simulated. The forest characteristics used for
all simulations were those measured for the deciduous
forest canopy in band 1 as reported above unless stated
otherwise. The leaf reflectance and transmittance values
were 0.056 and 0.051, respectively. The leaf inclination
distribution is reported in Fig. 3. The soil reflectance was
0.20. Six layers were simulated, with the bottom layer
(representing the understory-ground vegetation) always
having an LAI of 1.5. Leaf densities were varied according to tree spacing. A number of tree crown (or clumps
of tree crowns) spacings were simulated as shown in Fig.
6. Each tree crown or clump of trees is five layers high
above the understory layer 1. In the nadir direction, the
286
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE
SENSING,
VOL. GE-24, NO. 2, MARCH 1986
(b)
180
90,
90
75
75
60
60v
45°
45'
30'
30"
15½
15'
canopy in the visible band for four different su
30-
pi
ae
30'
30'
45'
45
180
45
150
135'
75'
W.
1235'75
7.
180
150
Directional percent reflectance distribution of the hardwood forest
canopy
in the visible band for four different
sun
angles.
The
system is described in Fig. 1. The solar position is shown as
starred circle
on
each plot.
plotting
a small
probability of gap through the canopy to the soil for simulation A and D was 0.21 and 0.096, respectively.
RESULTS AND DISCUSSION
Figs. 7-10 show the measured directional reflectance
distributions of the hardwood and pine forest canopies in
the visible and near-infrared (IR) bands, respectively.
When comparing these distributions to the measured and
simulated distributions of homogeneous agricultural crops
and natural vegetation canopies reported by Kimes et al.
[5]-[8], the reflectance distribution trends and dynamics
are very similar. All of the same significant radiant transfers that take place in these homogeneous canopies take
place in the forest canopies. These include 1) strong ansotropic scattering properties of the soil, 2) the geometric
effect of the vegetation probability of gap function on the
soil anisotropy, and 3) the anisotropic scattering of vegetation that is controlled by the phase function, leaf optical properties, and geometric "effect 1. " All of these
are described by Kimes et al. [6], [7]. These phenomena
account for the major scattering behavior of homogeneous
vegetation canopies and apparently of very dense forest
canopies such as measured in this study.
The model simulations support this claim. For examIII.
Fi.875'
90,
900
180,
Fig.
30
452
51
75
90,
180"
15
Fig. 8. Directional percent reflectance distribution of the hardwood fo.rest
canopy in the near infrared band. Symbols follow Fig. 7.
ple, Fig. 11 shows a comparison between two vegetation
canopies with the same total leaf area index (LAI = 4.2).
Both canopies were identical, in that the canopy characteristics (leaf and soil optical properties, LAI, and leaforientation distribution) were the same as that measured
for the hardwood canopy. The only difference between
the two simulations is that one canopy was completely
homogeneous (Fig. 6, simulation pattern E) and one had
large regions of clumped vegetation (Fig. 6, simulation
pattern D) that one might expect in dense forest canopies
such as measured in this study. We can see that the two
simulation patterns D and E, representing a dense deciduous forest canopy and the equivalent homogeneous canopy, respectively, are very similar in their reflectance distributions.
Fig. 12 shows a quantitative comparison between the
reflectance in the principal plane of the sun for the forest
canopy simulation and the equivalent homogeneous case.
For most purposes there is an insignificant difference between the dense forest canopy and the equivalent homogeneous scene. The small difference that does occur at
each sun angle (Fig. 10) is due to the clumping of vegetation in individual tree crowns or groupings of tree
crowns in forest canopies. This clumping has two effects
on the radiant transfers within the canopy: A) it increases
the probability of gap to the understory and/or soil layers,
287
KIMES et al.: DIRECTIONAL REFLECTANCE DISTRIBUTIONS
90
/
75
135
755 /
135s
90,
90,
1s0,
180
can-
Fig. 10. Directional percent reflectance distributions of the pine forest
canopy in the near infrared band. Symols follow Fig. 7.
which increases the influence of these lower layer's scattering properties, and B) it increases the number of low
transmitting clumps of vegetation within the scene causing increased backscatter and decreased forward scatter to
occur relative to the homogeneous case. These two effects
are terrned phenomenon A and B, respectively, throughout the paper.
The physics of phenomenon B is similar to the physics
involved in the increased backscatter and decreased forward scatter observed in organic soils and wintering deciduous forest canopies with no leaves. These canopies
have opaque vertical components that cause large azimuthal variations in scattering. For example, soils have
vertical components that have very low transmittance, and
thus dark shadowing of scene components occurs. In the
antisolar direction (backscatter toward the sun) only those
surfaces that are in direct sunlight are viewed by the sensor, and thus the reflectance is maximum in this direction.
As the sensor direction moves away from the antisolar
direction, the following two mechanisms cause the reflectance to decrease. 1) In the sensor's field of view, the
relative proportion of shadowed surfaces increases. 2) In
the sensor's field of view, the proportion of particle facets
with normals that deviate from the solar direction increase, causing decreased solar irradiance on these facets
(cosine function). Thus, organic soils exhibit strong back-
scatter and weak forward scatter. These trends are discussed in more detail and supported by data in [20]-[22].
The physics of phenomenon B are similar to soil canopies
in that the clumping of vegetation into individual tree
crowns (or contiguous tree crown groupings) create vertical structures that have relatively low transmission causing strong backscatter and weak forward scatter. The
magnitude of phenomenon B is not as dramatic in forest
canopies as it is in soils, however, since the vertical
clumps of vegetation are not opaque as in the case of soil
particles. Phenomena A and B become important in sparse
forest canopies as discussed in detail later.
The measured data of the hardwood canopy in band 1
(Fig. 7) compares relatively well in trend and magnitude
with the simulated pattern D (Fig. 11) if one compares
near equal solar zenith angles. However, the reflectance
in the backscattered direction (00 azimuth-away from the
sun) does not increase as rapidly in the simulated data as
compared to the measured data. For these canopies the
LAI is sufficiently high that the anisotropic soil has little
effect on the reflectance distribution above the canopy.
Canopies ranging between erectophiles and spherical leaforientation distributions with leaf reflectance approximately equal to leaf transmittance have the minimum reflectance more toward nadir at all sun angles, and a greater
increase in reflectance with increasing off-nadir view an-
Fig. 9. Directional percent reflectance distribution of the pine forest
opy in the visible band. Symbols follow Fig. 7.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, VOL. GE-24, NO. 2, MARCH 1986
288
SIMULATION PATTERN D
(a)
(c)
(b)
90'
s90
90' L
180'
SIMULATION PATTERN E
(d)
(e)
9W
W
(f)
80'
70'
60'
50o
4W
30'
20'
10'
0'
10
20"
30'
40
50 (
60
70
80
90
180
L._.
190
90
180
Fig. 11. Comparison of reflectance distributions in the visible band between two vegetation canopies with the same total LAI (4.2). For each
canopy, three plots with different sun angles are shown. Both canopies
were identical in that the canopy characteristics (leaf and soil optical
properties, LAI, and leaf-orientation distribution) were the same as that
measured for the hardwood canopy. The only difference between the two
simulations is that one canopy was completely homogeneous (simulation
pattern E, Fig. 6) and one had large regions of clumped vegetation (simulation pattern D, Fig. 6), which might be expected in dense forest canopies.
gle for all azimuth view directions and all sun angles relative to a more planophile canopy. A planophile canopy
has a minimum reflectance region that is shifted further
away from nadir in the forward scatter direction (00 azimuth direction) relative to erectophile-spherical canopies
[7]. The data and findings of Kimes [7] suggest that the
hardwood forest canopy has a more spherical-erectophile
average leaf-orientation distribution rather than the near
planophile distribution measured on a few isolated trees
under 4 m in this study. In fact, Hutchinson et al. [23]
289
KIMES et al.: DIRECTIONAL REFLECTANCE DISTRIBUTIONS
12
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11
10
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Fig. 13. Directional percent reflectance distribution of a simulated sparse
forest using simulation pattern A in the visible band. Total LAI was 2.3.
The bottom most layer (#1) had an LAI of 1.5 representing understory
vegetation and the area containing tree material had an LAI of 3.4. All
other canopy characteristics were the same as those measured for the
hardwood forest.
20° 400
-80° -600 -400 -200 -o0
OFF NADIR VIEW ANGLE
IN PRINCIPAL PLANE OF SUN
600
80°
Fig. 12. Directional percent reflectance in the principal plane of the sun
for simulation pattern D with the hardwood canopy characteristics and
the equivalent homogeneous case in the visibie band. Off-nadir view angles with negative values represent the backscatter direction and positive
values the forward scatter direction.
have shown leaf-orientation distribution of their east Tennessee deciduous forest canopy to be plagiophile, which
is a distribution between planophile and erectophile.
In summary, analyses of the simulated and measured
data suggest that the same major physicai phenomena operating in agricultural crops and natural vegetation communities operate in dense forest canopies. The clumping
of vegetation in tree crowns of very dense forest canopies
as studied here, is insignificant in terms of directional reflectance when the field of view covers several tree
crowns.
As the density of the forest canopy decreases one would
expect major differences in the scattering behavior of forest canopies relative to homogeneous agricultural and nat-
ural vegetation communities. As the density of the canopy
decreases, large openings begin to occur between individual tree crowns and/or clumps of tree crowns. With these
changes in the geometrical structure, one would expect to
see significant changes in the scattering behavior of the
canopy.
This change in scattering behavior was simulated by the
model simulations. For example, Fig. 13 shows simulated data of a very sparse forest canopy with large openings between individual tree crowns. Simulation pattern
A (Fig. 6) was used with a total LAI of 2.3. The bottom
layer had an LAI of 1.5 representing understory vegetation and the area (cells) containing tree material had an
LAI of 3.4. The leaf-orientation distribution, leaf and soil
optical properties were the same as those measured for the
hardwood forest. Fig. 13 shows an increased backscattering and decreased forward scatter component relative to
the dense forest canopy (Fig.- 11, simulation pattern D).
At first glance one would expect this difference to be due
to the large natural openings between the tree crownsphenomenon B as discussed previously. However, further
analysis shows the following.
Fig. 14(a) shows the reflectance in the principal plane
of the sun of various simulations for a solar zenith angle
of 20°. The figure clearly shows strong backscatter and
weak forward scatter of the sparse forest canopy (simulation pattern A) relative to the dense forest canopy (simulation pattern D, Fig. 11(a)). The leaf area (LAI = 3.4)
of the tree crowns in simulation pattern A was redistributed evenly in all cells (layers 2-6, layer 1 in all cases
has a homogeneous LAI of 1.5) to simulate the equivalent
homogeneous canopy (Fig. 14(a)). It is clear that the
"vegetation clumping" that occurs in sparse forest canopies causes a significant increase in backscatter relative
to forward scatter. To explore the cause of this effect we
turned the soil scattering off by making the soil black (absorption of 1.0). Comparing simulation A with the equivalent homogeneous case (both cases with black soil, Fig.
14(a)), we see that the "clumping of vegetation" causes
a modest increase in vegetation backscatter relative to forward scatter. Thus, phenomenon B is responsible for a
small portion of the increased backscatter in simulation
A. The remaining increase in backscatter in simulation A
as compared to the homogeneous case is due to the increase in probability of gap to the soil as a result of vegetation clumping (referred to as phenomenon A previously). For example, the probability of gap throughout
the canopy to the soil at 200 is 0.21. for simulation A and
0.13 for the equivalent homogeneous case. This causes
an increase in backscatter with a peak at the hot spot
(-20° off nadir view angle) due to the increase in directly
viewed, highly reflective, and directly illuminated soil.
Thus, in these particular simulations, the clumping of
vegetation in sparse forest canopies significantly alters the
probability of gap function that in turn permits the scattering properties of the substrate to be expressed to a larger
degree. Relative to these changes, the change in the scattering properties of the vegetation was secondary.
It is interesting to note that the peak reflectance of sim-
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, VOL. GE-24, NO. 2, MARCH 1986
290
U
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40°
200
00
600
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800
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-400 -200
00
200
4Q0
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80°
OFF-NADIR VIEW ANGLE
IN PRINCIPAL PLANE OF THE SUN
OFF-NADIR VIEW ANGLE
IN PRINCIPAL PLANE OF THE SUN
(b)
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5.0
4.0
3.0
2.0
-800
-600
-400
-200
00
200
400
600
80°
OFF NADIR VIEW ANGLE
IN PRINCIPAL PLANE OF THE SUN
(c)
Fig. 14. Directional percent reflectance in the principal plane of the sun
for simulation pattern A and the equivalent homogeneous case for a solar
zenith angle of (a) 200, (b) 50°, and (c) 80° in the visible band. Simulation pattern A characteristics are described in Fig. 13. The figure also
shows simulations where the soil was made black (absorption of 1.0) for
both simulation pattern A and the equivalent homogeneous case. Finally
simulation A is shown with Lambertian soil.
Fig. 14(b) shows the analysis of simulation A and the
ulation A with black soil occurs at -40° (Fig. 14(a)). The
position of this peak is due to a balancing between the equivalent homogeneous case for a solar zenith angle of
geometrical effect 1 and the vegetation phase function of 50. The same principles as discussed for the 200 solar
zenith simulation (Fig. 14(a)) apply for the 500 solar zethe canopy as discussed by Kimes et al. [6], [7].
291
KIMES et al.: DIRECTIONAL REFLECTANCE DISTRIBUTIONS
nith angle simulation. Fig. 14(c) shows the 80° solar zenith angle simulation. Because of the low sun angle the
soil has little influence on the scattering behavior of the
canopy. The canopy scattering behavior for all simulations in Fig. 14(c) is characteristic of any moderately
dense, homogeneous canopy at a large solar zenith angle-minimum reflectance near nadir and large increase
in reflectance with increasing off-nadir view angle for
all azimuth view directions. The effect that vegetation
clumping of sparse forest canopies has on the scattering
behavior of the canopy (phenomenon A and B) is greatly
diminished at large solar zenith angles.
Fig. 14(a) and (b) also shows simulation A with Lambertian soil. The comparison between the Lambertian and
non-Lambertian soil cases shows the importance of the
non-Lambertian soil reflectance function in influencing the
reflectance of the canopy as a whole. The non-Lambertian
reflectance function of the soil becomes more non-Lambertian as the solar zenith angle increases [2], [8], [201[231. However, as the solar zenith angle increases, the
probability of gap to the soil decreases causing a decrease
in the contribution of scattered flux from the soil to the
sensor.
One would expect that as the vegetation density of the
understory increased, the probability of gap to the soil
would become very small; phenomenon A would become
insignificant and only phenomenon B would be expressed.
Furthermore, as the leaf density increases in individual
tree crowns, the magnitude of phenomenon B would increase. These trends are shown in Fig. 15 for a solar zenith angle of 50°. The simulations were the same as simulation A discussed above except that layer 1 LAI was
increased to 4.0 and the LAI within individual tree crowns
was increased to 10.0 (one-sided projection), which means
an LAI of 2.0 for individual cells within the tree crown.
The high LAI in the individual tree crowns would be typical for sparse conifer stands. In general, conifer stands
have a much higher leaf area index as compared to broadleaved forests. Tadaki [24] reports that a reasonable range
of LAI for evergreen forests is 15 to 20 and for deciduous
broad-leaved forest 4 to 6 where the leaf area is reported
on a one-sided basis for broad-leaved species and on allsides basis for needle-leaved species. The leaf-orientation
distribution as well as the leaf reflectance and transmittance values measured in this study on the hardwood canopy are close to those measured for lodgepole pine by
Kimes et al. [25], [26]. There is evidence presented by
Kimes et al. [25] that this leaf distribution may be characteristic of a large class of needle bearing species. So
Fig. 15 is reasonable for a sparse conifer canopy with a
dense understory. The probability of gap to the soil is
small and, thus, the scattering properties of the soil are
insignificant in both simulation pattern A and the equivalent homogeneous case. Fig. 15 shows that by making the
soil black there is essentially no change in the scattering
behavior of the canopy. However, a relatively large increase in backscatter relative to forward scatter by the
vegetation itself due to the clumping of vegetation into
5.0
4.5
C-J
4.0
z
H
C-C
3.5
*L.
z
H
UL
3.0
2.5
2.0
200 400
oo
-800 -600 -400 -200
OFF-NADIR VIEW ANGLE
600
800
IN PRINCIPAL PLANE OF THE SUN
Fig. 15. Directional percent reflectance in the principal plane of the sun
for simulation pattern A and the equivalent homogeneous case for a solar
zenith angle of 500 in the visible band. Canopy characteristics as described in Fig. 13 were used except that layer I LAI was increased to
4.0 and the LAI within individual tree crowns was increased to 10.0.
This canopy is typical for a sparse conifer canopy with a dense under-
story.
tree crowns is apparent by comparing simulation pattern
A with the equivalent homogeneous case. Thus, in such
canopies phenomenon A is insignificant and phenomenon
B becomes very significant.
Fig. 16 shows how phenomenon A and B combined
cause a significant increase in backscatter relative to forward scatter with decreasing forest density. The total LAI
for each simulation pattern is reported in Fig. 16. In all
cases, the leaf-orientation distribution used were those
measured for the hardwood canopy. Furthermore, in all
simulation cases the LAI in layer 1 was 1.5-representing
a homogeneous understory-and the LAI in each tree
crown was always 3.4. Fig. 16 clearly shows the progressive increase in backscatter relative to forward scatter
as a result of increased vegetation clumping and de-
creased total leaf density.
IV. CONCLUSIONS AND IMPLICATIONS
Within the remote sensing community the authors have
heard several researchers hypothesize that forest canopies
may behave very differently from agricultural crops because of their unique structure-tall canopy height and
open spacings between the upper crowns of individual
trees. Measurements and model simulations in this study
show that the directional scattering behavior of relatively
dense forest canopies is very similar to the directional
scattering behavior of agricultural crops and natural
grasslands. The most significant physical phenomena involved in these dense canopies where the soil contribution
is minimal is the anisotropic scattering of vegetation,
292
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, VOL. GE-24, NO. 2, MARCH 1986
5.0
the probability of gap to the soil is small. In such cases,
only phenomenon B is responsible for the increased backscatter relative to the forward scatter when compared with
the equivalent homogeneous canopy. In this study, the
peak backscatter was increased due to phenomenon B as
much as 35 percent relative to the equivalent homogeneous case. The magnitude of phenomenon B increases as
the leaf density in the tree crowns increases. The magnitude of this phenomenon would be generally larger in conifer forests as opposed to broad-leaved forests since individual conifer trees generally have a higher leaf density.
In sparse forest canopies the combined effect of phenomenon A and B can typically cause a 65-percent or higher
increase in the peak backscatter relative to the equivalent
homogeneous canopy. It is hypothesized that these phenomena are important in sparse conifer forests, such as
the boreal forests, which account for 50 percent of the
LU
H.
2.)
cr 3.0
IN P
Fig.16.Dirctinalperentrefetneih
2.5
W~~~~~~F-AI VIE
ANL
world's forest area.
2 0
-800
-600
-400
-200
00
200
400
600
800
OFF-NADIR VIEW ANGLE
IN PRINCIPAL PLANE OF THE SUN
Fig.
16.
Directional percent reflectance in the
principal plane
of the
sun
for simulation patterns A, B, C, and D. The visible band is reported for
a 200 solar zenith angle. The total LAI and percent crown cover for
simulation patterns A, B, C, D were 2.3, 3.0, 3.5, 4.2, and 25, 44, 59,
79 percent, respectively. The leaf orientation distribution were the same
as measured for the harwood forest. In all simulations, the LAI in the
lower layer was 1.5-representing a homogeneous understory-and the
LAI in each tree crown was always 3.4.
which is controlled by the phase function of the leaf orientation distribution, the leaf optical properties, and the
geometric effect 1 as discussed by Kimes et al. [6], [7].
Only in more sparse forest canopies with significant
spacing between the tree crowns (or clumps of tree
crowns) does the general scattering behavior deviate from
homogeneous agricultural and natural grassland canopies.
This clumping has two effects on the radiant transfers
within the canopy: A) it increases the probability of gap
to the understory and/or soil layers causing an increase in
the influence of the scattering properties of these lower
layers and B) it increases the number of low transmitting
clumps of vegetation within the scene causing increased
backscatter and decreased forward scatter to occur relative to the homogeneous case.
In sparse forest canopies, phenomenon A is clearly
dominant in forest canopies where the clumping of tree
crowns is such that a large probability of gap occurs to
substrate layers (e.g., soil, litter, snow, or understory
vegetation cover) that have scattering properties significantly different from forest vegetation. In the case where
the substrate is soil (as presented in this study), the backscatter is greatly increased in relation to the forward scatter. In this study, the peak backscatter was increased as
much as 30 percent relative to the equivalent homogeneous canopy case. Phenomenon B adds to this effect by
further increasing the backscattering relative to the forward scatter. Phenomenon A becomes insignificant when
the understory vegetation has similar scattering properties
as the forest vegetation and is significantly dense so that
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[2] J. A. Smith, "Matter-energy interaction in the optical region," in
ASP Manual of Remote Sensing, 2nd ed., ch. 3, 1983.
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[16] W. Mendenhall, L. Ott, and R. L. Scheaffer, Elementary Survey Sampling. Belmont, CA: Wadsworth Publishing, 1971.
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M. E. Bauer, "Soybean canopy reflectance as a function of view and
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(Ann Arbor, MI), May 1981.
[18] S. Chandrasekhar, Radiative Transfer. New York: Dover, 1960.
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[19] C. L. Walthall, J. M. Norman, J. M. Welles, G. C. Cambell, and
B. L. Blad, "A simple equation to approximate bidirectional reflectance from vegetative canopies and bare soil surfaces," Appl. Opt.,
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[20] K. L. Coulson, "Effects of reflection properties of natural surfaces in
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Tajchman, and J. M. Norman, "The architecture of an east Tennessee deciduous forest canopy," J. Appl. Eco., 1985.
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1980.
*
W. Wayne Newcomb received the B.S. degree in
agronomy from the University of Maryland in
1974.
Since 1978, he has been a data analyst for RMS
Technologies at the NASA Goddard Space Flight
Center, primarily working with NASA scientists
evaluating Thematic Mapper Simulator data for
agricultural use. He also has worked with AVHRR
data on the GIMMS Project to monitor African
grasslands.
K
Ross F. Nelson received the B.S. degree in forest
j management from the University of Maine,
Orono, in 1974, and the M.S. degree in forestry/
remote sensing from Purdue University, West Lafayette, IN, in 1979.
Since 1979, he has been a Physical Scientist in
m, the Earth Resources Branch at the NASA Goddard
Space Flight Center, involved with the use of digital remotely sensed data for assessing the forest
canopy. He has worked with Landsat MSS data to
evaluate gypsy moth damage, Thematic Mapper
Simulator data to evaluate spruce budworm damage, and AVHRR and MSS
data to assess deforestation in South America. He has worked with geobotanical scientists to determine the effects of mineralization on forest canopy condition, and is investigating the utility of laser-induced fluorescence
for tree species identification.
R=
*
Daniel S. Kimes was born in Columbus, OH. He
received the B.S. degree in wildlife biology from
Colorado State University, Fort Collins, in 1975,
the M.S. degree in remote sensing from the University of Michigan, Ann Arbor, in 1976, and the
Ph.D. degree in earth resources from Colorado
State University, in 1979.
Since 1979, he has been employed at the NASA
Goddard Space Flight Center, Earth Resources
Branch, Greenbelt, MD, where he has been engaged in mathematical modeling of visible, near
infrared, and thermal infrared radiation interactions with vegetation.
, _~
John B. Schutt was born in New Haven, CT. He
received the B.S. degree in chemistry for the Massachusetts Institute of Technology, Cambridge, in
1952, M.S. degrees in physical chemistry and
chemical engineering in 1956, and the Ph.D. degree in chemical engineering from the University
of Rochester, Rochester, NY, in 1958.
Since 1973, he has been employed at NASA
Goddard Space Flight Center, Earth Resources
Branch, Greenbelt, MD, where he has been engaged in studying the dynamic behavior of vegetation and its radiometric manifestations.