Digital Landsat Thematic Mapper (TM) and Satellite Probatoire d'Observation de la Terre (SPOT) High Resolution Visible (HRV) images of coniferous forest canopies were compared in their relationship to forest wood volume using correlation and regression analyses. Significant inverse relationships were found between softwood volume and the spectral bands from both sensors (P less than 0.01). The highest correlations were between the log of softwood volume and the near-infrared bands (HRV band 3, r = -0.89; TM band 4, r = -0.83)

Isaacson, Dennis L.

Paine, D. P.

Ripple, William J.

Wang, S.

English

NASA Technical Reports Server

INT. J. REMOTE SENSING, 1991, VOL. 12, NO. 9, 1971-1977 N95- 24097
A preliminary comparison of Landsat Thematic Mapper and SPOT-I
HRV multispectral data for estimating coniferous forest volume
?F
W. J. RIPPLE, S. WANG, D. L. ISAACSON and D. P. PAINE
Environmental Remote Sensing Applications Laboratory,
Department of Forest Resources, Oregon State University,
Corvallis, OR 97331, U.S.A.
(Received 26 February 1991)
Abstract. Digital Landsat Thematic Mapper (TM) and Satellite Probatoire
d'Observation de ia Terre (SPOT) High Resolution Visible (HRV) images of
coniferous forest canopies were compared in their relationship to forest wood
volume using correlation and regression analyses. Significant inverse relationships
were found between softwood volume and the spectral bands from both sensors
(P < 0-01). The highest correlations were between the log of softwood volume and
the near-infared bands (HRV band 3, r= -0"89; TM band 4, r= -0.83).
I. Introduction
The potential to extract forest volume information from remotely sensed satellite
sensor data could substantially aid inventories of forest resources. In this study, we
examined the capabilities of both the Landsat Thematic Mapper (TM) and SPOT
HRV multispectral satellite data to extract forest volume information. The TM has
six reflective spectral bands with 30 m spatial resolution plus a thermal band, while
SPOT has three reflective spectral bands with 20 rn resolution (table 1).
There is normally an inverse relationship between vegetation amount and both
visible reflectance (0-4 to 0-7/_m) and middle-infrared reflectance (1.3 to 2-6_m)
because of highly absorbing plant pigments and strong absorption from water in the
leaves respectively (Curran 1980, Ripple 1986). Conversely, near-infrared reflectance
typically has a direct relationship with vegetation amount because of scattering and
little or no absorptance (Curran 1980). Researchers have found that this direct
relationship in the near-infrared with increasing amounts of conifer biomass does not
always exist. The near-infrared response can be flat (Franklin 1986, Peterson et al.
1987) or inverse (Danson 1987, Spanner et al. 1990) depending upon the amount and
brightness of exposed understory or background.
A number of researchers have evaluated the potential for using SPOT HRV data
(Danson 1987, De Wulf et al. 1990, Hame et al. 1988), TM simulator data (Franklin
1986), and TM data (Horler and Ahem 1986, Spanner et al. 1990) for estimating
forest stand parameters, but only one study was found that compared SPOT and TM
for monitoring forest stand parameters (Brockhaus et al. 1988). In the comparison
study, several TM and SPOT bands were significantly correlated with basal area and
stand age in coastal plain forests of North Carolina, although the authors concluded
that these correlations were too low to be used in developing a predictive model
(Brockhaust et al. 1988). Chavez and Bowell (1988) compared the spectral informa-
tion content of TM and HRV data over geologic, urban, and agricultural sites in
0143-1161/91 $3.00 © 1991 Taylor & Francis Ltd
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Remote Sensin 9 Letters 1973
Arizona, U.S.A., and found the TM data contained more spectral information than
the SPOT multispectrai data, with TM bands showing field/soil information not
available in the SPOT bands.
The objectives of our study were to compare information content of digital TM
and HRV muitispectral data over a forested test site and to examine the relationships
between the sensor data and softwood volume. The study site was in McDonald State
Forest located in the foothills of the Coast Range mountains just outside of Corvallis,
Oregon (centred on 44°37'N lat., 122 ° 19' long.). This forest is managed by the
College of Forestry at Oregon State University. Douglas-fir (Pseudotsuga menziesii) is
the dominant tree species. The study area consists of young forest plantations
established after clearcutting and older forest stands (range 25-148 years old). Both
deciduous shrub and herb understory are common throughout the forest. Precipi-
tation averages 107cm per year with most of it falling as rain from October through
May.
2. Methods
The satellite sensor images were from a 25 July, 1988 HRV scene and a 30 July
1988 TM scene. The SPOT data were obtained at a + 10-7 ° incidence angle and
processed at level lB. Sub-areas representing approximately the same geographic
areas were extracted from each image using the following procedure. Screen images of
both the TM and HRV images were projected to permit selection of a set of pixels
representing the same ground features. For each feature, a pair of x, y locations were
determined, one from the HRV data and one from the TM data. A least-squares
multiple regression was used to develop a transformation model (lsaacson et al.
1987). This approach permitted the direct transformation between TM image space
and HRV image space without a resampling or rectification to a common scale or
projection, thereby better preserving the original data values for more direct
comparison of the study area images. The study area consisted of 169 rows and 251
columns of HRV data and 120rows and 176columns of TM data. No corrections
were made for path radiance.
Forty-six forest stands were delineated on both the TM and HRV screen images.
The delineations were made at locations of approximately one pixel width in from the
edge of the stands to reduce edge effect. The mean pixel digital number (DN) was
calculated for each band for each of the 46 stands for both the TM and HRV data.
Softwood volumes ranged from 28.3 to 839-5 m 3 ha-1. Forest stand areas ranged
from 2-1 to 74.1 ha. The number of interior pixels in the 46 stands ranged, from 12 to
566 for the HRV data and 3 to 258 for the TM data.
A correlation analysis was conducted on all spectral bands from both image
subsets (n=46). Softwood volume data were obtained from permanent timber
inventory plots established and maintained by College of Forestry staff, with volumes
determined using local tarif tree volume computation guidelines. Softwood volume
represents the volume of wood in the bole of the conifers and is expressed in cubic
metres per hectare (m 3 ha-1). Correlation, simple, and multiple regression analyses
were conducted on the spectral band/softwood volume data set.
3. Results and discussion
All of the spectral bands were highly correlated, both within and between the TM
and HRV data sets (table 2). Because of the relatively homogeneous forested area in
our test site, the correlation coefficients between any two bands tended to be higher
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Remote Sensin 9 Letters 1975
than would normally be expected. This was also found to be the case with spectrally
homogeneous areas analysed by Chavez and Berlin (1984). Similar inverse relation-
ships were found between softwood volume and the spectral bands from both sets of
data. The correlation coefficients ranged from -0.63 to -0-82 for HRV bands and
from -0-40 to -0-76 for TM bands. The slightly higher correlations for the SPOT
data may be due to differences in spatial resolution, the wavelength range or position
of the bands, or the signal to noise ratios. The higher spatial resolution of the SPOT
HRV may be more sensitive to vegetation density than the lower spatial resolution of
the TM data set, although the correlation coefficient is larger for TM band 3 (-0-69)
when compared to HRV band 2 (-0.63). An examination of the scatter diagrams
revealed inverse curvilinear relationships between many of the spectral bands and
softwood volume.
Log transformations of softwood volume increased the correlation coefficients for
a number of the HRV and TM bands (table 2). This was probably due to the
asymptotic nature of these relationships as tree volumes increased in older stands with
higher canopy closure (Ripple 1985). Figure 1 shows similar relationships for both
TM band 4 with log volume (r=-0-83) and HRV band 3 with log volume
(r = -0-89). These results confirm those found by Danson (1987), where HRV band 3
was inversely related to mean height (r = -0-83) of conifers and DeWulf et al. (1990)
who found HRV band 3 to have the strongest relationship with forest stand
parameters. We speculate that these inverse relationships were caused by (I) increased
canopy shadowing within larger older stands, and (2) decreased understory brightness
with the closing of the conifer canopy (Spanner et al. 1990).
With the HRV data, stepwise multiple regression (P _<0.05) resulted in only one
independent variable (band 3) for predicting log softwood volume (r = -0-89). With
the TM data set, bands 4 and 2 were significant using the stepwise procedure with log
softwood volume as the dependent variable (R z = 0-73). The simple regression of log
volume on TM band 4 alone explained just slightly less variance (r z = 0-69) than the
stepwise procedure above. This indicates that the additional spectral bands available
on the TM sensor may not contribute significantly to increasing the accuracy of
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Figure 1. Scatter diagrams and results of regressing softwood volume on both TM band 4 and
HRV band 3 for the McDonald Forest study site. Each point on the scatter diagrams
represents I of 46 observations.
1976 I4/.J. Ripple et al.
estimates of softwood volumes in relatively homogeneous Douglas-fir stands. Simple
regression is preferred because when spectral bands are used as independent variables
in multiple regression, intercorrelations among spectral bands can affect the regres-
sion coefficients and the sum of squares. It should be noted that the regressions in
figure 1 involving softwood volumes were intended only for the comparison of HRV
with TM data. In actual applications, line fitting corrections may be necessary
(Curran and Hay 1986).
We believe the success of using the single-infrared bands to determine softwood
volume was due to the following:
(a) Since the understory had a highly reflective shrub and herb layer, the young
open conifer stands with low softwood volume had higher radiance than the
older voluminous stands having more shadows, thus causing the strong
inverse relationships.
(b) The dynamic range of the infrared bands were higher than the other spectral
bands.
(c) The study area was relatively homogeneous and dominatd by Douglas-fir
trees. In study areas that are heterogeneous with more than one vegetation
cover type, multiband data sets will probably be critical for estimating
softwood volume.
Under the conditions of this study, the following conclusions can be drawn:
(i) Both HRV and TM data sets exhibited high band to band correlations.
(ii) Both HRV and TM data showed similar significant inverse relationships with
softwood volume.
(iii) We speculate that the inverse relationships were caused by canopy shadowing
and/or the extent of the bright deciduous understory exposed to the sensor.
(iv) With the exception of the red bands (HRV band 2 and TM band 3), the HRV
data had slightly higher correlations with softwood volume than the TM
data. This was probably due to the higher spatial resolution of the HRV
although differences in the location and width of the bands or the signal to
noise ratios could also be factors.
(v) The near-infrared band was the most accurate for predicting softwood
volume from both the HRV and the TM.
(vi) We speculate that inverse curvilinear relationships between spectral bands
and softwood volume were caused by asymptotic charactristics of the forest
canopy/spectral relationship.
Acknowledgments
This project was funded in part by NASA grant number NAGW-1460, the
Oregon Forest Research Laboratory, and the Coastal Oregon Productivity Enhance-
ment (COPE) program, Corvallis, Oregon. We thank Debora L. Cummings and Mike
D. Lent for providing information and stand data from the Oregon State University
Research Forest database.
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Remote Sensin 9 Letters 1977
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A preliminary comparison of Landsat Thematic Mapper and SPOT-1 HRV multispectral data for estimating coniferous forest volume

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