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A New Approach to Derive Canopy Structure Information for
Boreal Forests Using Spectral BRDF Data
Stefan R. Sandmeier* and Donald W. Deering+
* USRA
/ NASA Goddard Space Flight Center, Biospheric Sciences Branch, Code 923, Greenbelt, MD 20771
Voice: (301) 286-0780, Fax: (301) 286-0239, Email: [email protected]
+ NASA Goddard Space Flight Center, Biospheric Sciences Branch, Code 923, Greenbelt, MD 20771
INTRODUCTION
Recent studies of the spectral variability of BRDF effects
demonstrated the potential of using hyperspectral BRDF data
to derive vegetation canopy characteristics (e.g., [1], [2]).
Ground and remotely sensed BRDF data were successfully
used to relate surface structure characteristics of both grasslands and boreal forests to the spectral anisotropy index
(ANIX) ([2], [3]). ANIX is defined as the ratio of maximum
(Rmax) and minimum (Rmin) bidirectional reflectance factors
per spectral band in the solar principal (or defined azimuthal)
plane ([1], [2], [3]):
ANIX(λ,θ i ) =
R max ( λ )
R min ( λ )
[ dimensionless ],
(1)
In this study, we go one step further by taking advantage of
the relationship between hyperspectral ANIX and nadir reflectance data. This relationship exists due to multiple scattering
effects in the vegetation canopies and is strong for erectophile
canopies with large canopy gap fractions while it is rather
weak for planophile vegetation structures [1]. As a first step
toward analyzing the canopy structural features, the ANIX to
nadir reflectance relationship was parametrized with third
degree polynomial functions (Fig. 1). The polynomial coefficients subsequently derived for each pixel were then used as
input to a minimum distance classifier in order to explore the
potential of hyperspectral BRDF data for land cover classification.
In a second step, we investigated the potential of spectral
BRDF data for deriving canopy structure characteristics when
a limited number of bands are available, such as in the case of
MISR or POLDER data. For this objective we introduced a
normalized difference anisotropy index (NDAX) which is a
surrogate for the spectral variability of BRDF effects. Similar
to the normalized difference vegetation index (NDVI), it is
derived from a red (maximum BRDF effects) and a near-infrared (minimum BRDF effects) band:
NDAX(θ i ) =
ANIX red ( θ i ) − ANIX nir ( θ i )
ANIX red ( θ i ) + ANIX nir ( θ i )
[ dimensionless ],
(2)
DATA SETS
Hyperspectral BRDF data used in this study were acquired
with the Advanced Solid-State Array Spectroradiometer
(ASAS) [4] over the southern fen test site (53°48’N /
104°37’E) of the Boreal Ecosystem-Atmosphere Study
(BOREAS) in Saskatchewan, Canada [5]. The data were
obtained in the solar principal plane on 21 July 1994 under a
solar zenith angle of 40° and at an altitude of 5500m. Acrosstrack ground pixel size was approximately 3.7m at nadir. The
data were subsequently atmospherically and geometrically
corrected [3]. ANIX and NDAX values were derived from
ASAS data from ±45° off-nadir view angle directions.
The test site covers six dominant boreal cover types including mature black spruce (Picea mariana Mill.), jack pine
(Pinus banksiana Lamb.), and tamarack (Larix laricina) forests, and treed and clear muskeg areas. The clear muskeg is a
fen, a type of wetland with marsh vegetation comprising vari9.0
8.0
a black spruce
b jack pine
c tamarack
d spruce / pine
e treed muskeg
f clear muskeg
a
d
7.0
anisotropy index (ANIX)
Abstract -- The spectral variability of BRDF effects in vegetation canopies was investigated with the anisotropy index
(ANIX) and a normalized difference anisotropy index
(NDAX). Both indices were related to the canopy structural
properties of six dominant cover types in the Canadian boreal
forests. High ANIX and NDAX values were associated with
erectophile canopies such as black spruce. Planophile canopies, such as fen areas, exhibited low ANIX and NDAX values. Compared to nadir reflectance data and the normalized
difference vegetation index (NDVI), the two new indices significantly improved the accuracy of land cover classification.
6.0
5.0
b
c
4.0
3.0
2.0
f
e
1.0
0.0
0.00
0.02
0.04
0.06
0.08
0.10
0.12
0.14
0.16
0.18
0.20
nadir reflectance
Figure 1: Anisotropy index (ANIX) data versus nadir reflectance for six dominant cover types in the BOREAS southern
fen site (53°48’N/104°37’E) acquired with ASAS in the solar
principal plane on 21 July 94 under 40° solar zenith angle.
Each line represents data from multiple spectral bands
ranging from 630 to 815nm.
ous sedge and herbaceous species as well as mosses. Fig. 2A
characterizes the forest cover of the test site with a map provided by the Saskatchewan Environment and Resource Management [3].
RESULTS AND DISCUSSION
The relationship between the canopy structure and the spectral variability of BRDF effects is demonstrated in Fig. 1. The
third degree polynomial functions representing the relationship between ANIX and nadir reflectance for each dominant
cover type showed r2 > 0.98, except for clear muskeg where r2
was 0.87. Each line represents data from 19 spectral bands
ranging from 630 to 815nm.
Due to the high number of gaps in the canopy, the erectophile spruce forest showed the strongest correlation between
ANIX and nadir reflectance (Fig. 1, curve a) and also produced the highest ANIX. In the clear muskeg area BRDF
effects were rather small and less dependent on nadir reflectance because the canopy gap fraction of the marsh and moss
vegetation in the fen is rather low (Fig. 1, curve f).
The constants of the polynomial functions illustrate the
strong contrast between erectophile spruce canopies and
planophile clear muskeg areas (Fig. 2D). The NDAX depicted
in Fig. 2E also captures the main characteristics of the canopy
clear
cut
A
+ mixed
species
structures even though it uses only two spectral bands. The
NDVI, however, does not clearly discriminate between the
clear muskeg and the mature forest areas (Fig. 2F). Fig. 3
which shows mean and standard deviation of NDVI and
NDAX for the six dominant cover types present in the test site
demonstrates that jack pine, spruce/pine, and clear muskeg
areas exhibit almost identical NDVI values, while the NDAX
are much lower for the clear muskeg than for the forested
areas. NDVI on the other hand better discriminates between
the deciduous (tamarack) and the coniferous (spruce and pine)
areas while NDAX values for pine and tamarack are almost
identical. This is partly due to the different information preserved by NDVI and NDAX. NDVI is based on nadir reflectance data and is therefore more greatly influenced by the
canopy understory characteristics. NDAX is derived from offnadir data, from ±45° viewing directions in our case, and is
thus influenced more by the canopy characteristics rather than
the understory. A combination of both, nadir reflectance and
ANIX data or NDVI and NDAX, is therefore expected to produce optimum land cover classification results.
The overall accuracies of a minimum distance classification
for various data sets are shown in Fig. 4. The minimum distance classifier is preferred to the maximum likelihood algorithm in this study because the reflectance distribution of the
B
C
E
F
black
spruce
jack
pine
spruce +
pine
tamarack
treed
muskeg
N
clear
muskeg
0
D
200m
Figure 2: BOREAS southern fen site (53°48’N/104°37’E): (A) 1:12,500-scale forest cover map from the Saskatchewan Environment and Resource Management, (B) results from a minimum distance classification using three ASAS nadir reflectance
bands (560nm, 671nm, 898nm), (C) same as (B) but classification also includes four polynomial coefficients representing the
relationship between 19 ANIX and nadir reflectance bands (630 to 815nm), (D) constants of polynomial functions, (E) normalized difference anisotropy index (NDAX) derived from two ASAS bands (671nm and 898nm) acquired in 45° forward- and
backward view directions, and (F) normalized difference vegetation index (NDVI) derived from two ASAS bands (671nm and
898nm) acquired in nadir direction. ASAS data were obtained under 40° solar zenith angle on 21 July 94.
1.0
0.9
0.8
NDVI and NDAX
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0.6
0.5
0.4
0.3
0.2
0.1
Normalized Difference Vegetation Index (NDVI)
0.0
Normalized Difference Anisotropy Index (NDAX)
-0.1
black
spruce
jack pine
tamarack
spruce/
pine
treed
muskeg
clear
muskeg
Figure 3: Mean and standard deviation of NDVI and NDAX
values for six dominant cover types in the BOREAS southern
fen site (53°48’N/104°37’E) acquired with ASAS in the solar
principal plane on 21 July 94 under 40° solar zenith angle.
boreal land cover types did not fully comply with the Gaussian assumption [3]. Training samples for the classification
were taken from the forest cover map which also served as
ground reference for assessing classification accuracies.
Using three nadir reflectance bands (560nm, 671nm,
898nm), the overall classification accuracy is about 31%. For
the NDVI the accuracy is even lower, about 24%. NDAX and
three ANIX bands (560nm, 671nm, 898nm) perform remarkably better and produce overall accuracies of 36% and 40%,
respectively. The polynomial coefficients, however, did not
further improve the classification accuracy, even though they
incorporate hyperspectral resolution. Best results were obtained when nadir and off-nadir data were combined. Coupling
NDVI and NDAX data resulted in an overall accuracy of 43%,
and the simultaneous use of three nadir reflectance and ANIX
bands (560nm, 671nm, 898nm) produced an accuracy of 44%.
The combination of polynomial coefficients and three nadir
reflectance bands increased the accuracy to 47%. Thus, com50%
overall classification accuracy
45%
40%
35%
30%
25%
20%
15%
10%
5%
pared to the results from NDVI, the classification accuracy
could almost be doubled (note: the generally low accuracies
observed here are due to the high number of classes discriminated and the generalization of the forest cover map).The
same trend of improved classification accuracies could be
observed with the kappa coefficients (not presented here).
Fig. 2C shows the quality of the classification for the combination of nadir reflectance and polynomial coefficient data.
Compared to using NDVI data alone (Fig. 2B), the combination of nadir reflectance and ANIX data clearly improved the
classification, especially of the clear muskeg (i.e., the fen)
area. Thus, the new approach of using spectral BRDF data for
land cover classification is very promising at least for the considered boreal landscapes.
CONCLUSIONS
The spectral variability of BRDF data is related to the
structural characteristics of vegetation canopies. Both ANIX
and NDAX allowed discrimination between erectophile and
planophile canopy structures in boreal land covers. Compared
to nadir reflectance and NDVI data, off-nadir data (i.e., ANIX,
polynomial coefficients relating ANIX and nadir reflectance,
and NDAX data) remarkably improved the quality of a boreal
land cover classification. The best classification results were
achieved when nadir and off-nadir data were combined.
REFERENCES
[1] St. Sandmeier, Ch. Müller, B. Hosgood, and G. Andreoli,
“Physical mechanisms in hyperspectral BRDF data of
grass and watercress,” Remote Sens. Environ., vol. 66,
no. 2, pp. 222-233, 1998.
[2] St. Sandmeier, E.M. Middleton, D.W. Deering, and W.
Qin, “The potential of hyperspectral BRDF for grass
canopy characterization,” J. Geophys. Res., in press.
[3] St. Sandmeier and D.W. Deering, “Structure analysis and
classification of boreal forests using airborne hyperspectral BRDF data from ASAS,” Rem. Sens. Envir., in press.
[4] J.R. Irons, K.J. Ranson, D.L. Williams, R.R. Irish, and
F.G. Huegel, “An off-nadir-pointing imaging spectroradiometer for terrestrial ecosystem studies,” IEEE Trans.
Geosci. Rem. Sens., vol. 29, no. 1, pp. 66-74, 1991.
[5] P.J. Sellers, F.G. Hall, R.D. Kelly, et al., “BOREAS in
1997: Experiment overview, scientific results, and future
directions,” J. Geophys. Res., vol. 102, no. D24,
pp. 28731-28769, 1997.
0%
NDVI
(2 b.)
nadir refl.
(3 b.)
NDAX
(2 b.)
poly. coeff.
(19 b.)
ANIX
(3 b.)
NDAX+
NDVI
(2+2 b.)
ANIX+ poly. c.+
nadir refl. nadir refl.
(3+3 b.) (19+3 b.)
Figure 4: Overall accuracy of minimum distance classification for various data sets derived from ASAS data for six
dominant cover types in the BOREAS southern fen site
acquired on 21 July 94 under 40° solar zenith angle. For
an explanation of the data sets see Figure 2. The number
of bands used are given in parentheses.
ACKNOWLEDGMENT
We thank Jim Irons, NASA/GSFC, for providing ASAS
imagery from BOREAS and Abdelgadir Abuelgasim, NASA/
GSFC, for atmospherically correcting the data. Stefan Sandmeier was financed by the Swiss National Science Foundation
(Grant No. 8220-050392) and by the USRA / Goddard Visiting Scientist Program (NAS-5-98181).