int. j. remote sensing, 2000, vol. 21, no. 1, 61±79
Incorporating texture into classi®cation of forest species composition
from airborne multispectral images
S. E. FRANKLIN², R. J. HALL³§, L. M. MOSKAL², A. J. MAUDIE²
and M. B. LAVIGNE¶
²Department of Geography, The University of Calgary, Calgary, Alberta,
Canada T2N 1N4
³Canadian Forest Service, Natural Resources Canada, Northern
Forestry Centre, Edmonton, Alberta, Canada T6H 3S5
¶Canadian Forest Service, Natural Resources Canada, Atlantic Forestry Centre,
Fredericton, New Brunswick, Canada E3B 5P7
(Received 17 February 1998; in ®nal form 2 February 1999 )
Although research with digital airborne remote sensing data has been
undertaken in dierent ecoregions to classify forested areas, the potential role of
such imagery in deriving information to assist forest management has not yet
been fully de®ned. The objective of this study was to determine the extent that
the addition of texture could improve spectral classi®cation of high spatial
resolution images (pixel size < 1m). These images represented pure and mixedwood forest stands from ecoregions in Alberta and New Brunswick, Canada. This
study employed a judicious, selective application of texture to stands within a
hierarchical classi®cation framework. In Alberta, the addition of texture made a
modest improvement in classi®cation accuracy from 60% to 65%. In New
Brunswick, the application of texture to selected land cover types resulted in an
overall 12% improvement in classi®cation accuracy. The addition of image texture
increased classi®cation accuracy for high spatial detail imagery relative to low
spatial detail imagery. Incorporating texture into classi®cation also improved
classi®cation accuracies for hardwood stands more so than for softwood stands,
but greater attention to stand structure and composition will be needed in future
work. Classi®cation accuracies on the order of 60±65% were achieved with simple
texture derivatives, maximum likelihood decision rules and conventional classi®cation methods.
Abstract.
1.
Introduction
The extent to which stand structure can be detected, classi®ed and mapped will
determine the informational value of classi®ed airborne images in the inventory and
management of the world’s forest resources. A forest stand is characterized by several
attributes that include species composition, crown closure, height and age class.
Examples from using remote sensing of airborne images are required to develop an
understanding of the roles such images and methods can play in forest inventory,
resource database development, forest management and modelling. These examples
§Author to whom correspondence should be addressed.
International Journal of Remote Sensing
ISSN 0143-1161 print/ISSN 1366-5901 online Ñ2000 Taylor & Francis Ltd
http://www.tandf.co.uk/journals/tf/01431161.html
62
S. E. Franklin et al.
must be linked to existing forest inventory systems, which are stand descriptions
consisting of the same structural attributes (i.e., species, crown closure, height) that
are currently derived from the manual interpretation of aerial photographs (Leckie
et al. 1995, Magnussen 1997).
It is generally acknowledged that digital remote sensing can provide information
that is not currently part of an existing forest inventory. One view that limits the
use of remote sensing in forestry, however, is that results of digital image analysis
should be comparable, or even superior, to the inventory results generated from
existing analogue methods. In addition, the image analysis methods employed must
be relatively simple, well-understood and readily accessible to managers responsible
for development and implementation of resource management plans within a framework of ecological, socio-economic and environmental considerations. The premise
of this study is that e orts toward an ecosystem-based approach to resource management, may be aided by the increasing availability of image processing software on
the `desktop’ that can be easily integrated with geographic information systems (GIS).
As Leckie et al. (1995: p. 337) have pointed out: `Use of digital high-resolution
(< 1 m) multispectral imagery as an alternative to aerial photography for forest
inventory mapping is a possible revolutionary innovation’. Previous studies in
Canadian forests (Treitz et al. 1985, Smith et al. 1991, Franklin et al. 1991, Heygi
et al. 1992, Franklin and McDermid 1993, Eldridge and Edwards 1993, St-Onge and
Cavayas 1995, 1997) and elsewhere (e.g., Baulies and Pons 1995), have indicated
that digital image processing methods applied to airborne images can aid in forest
inventory, but that much e ort is required before these procedures can replace or
even replicate existing inventories for a wide variety of reasons. In the long term, it
is also now apparent that more widespread use of digital remote sensing may not
occur simply because new applications, such as an assessment of stand productivity,
can be accomplished. For example, Leaf Area Index (LAI) is not currently considered
part of the existing forest inventory in Canada, and yet is an important structural
attribute of ecosystems and forest stands that is related to productivity. Is the lack
of LAI in the forest inventory a result of the fact that LAI cannot be estimated using
air photo interpretation methods? Or, that LAI represents an aspect of the forest
not directly related to timber volume, a basic inventory attribute in Canadian
forestry? The increasing demands for new, timely and reliable information, such as
LAI estimates, results in greater opportunities to develop new remote sensing
applications.
Forest inventory originates with a stand discrimination or classi®cation strategy
that is necessarily regional and narrow in scope (Leckie and Gillis 1995). In Canada,
this scope is de®ned provincially, and then combined nationally to provide annual
perspectives on the status of forest resources in Canada (Natural Resources Canada
1997, 1998). Each province has established an inventory classi®cation system (Gillis
and Leckie 1993) that results in mapped areas represented by polygons in a GIS
database. It is this inventory standard, or this de®nition of stand strata, that digital
remote sensing must attempt to emulate in a wide range of forest conditions. Only
then will more e ort be justifed to resolve some of the more obvious problems in a
full-scale adoption of the digital environment (Ryerson 1989, Pitt et al. 1997).
The onus is clearly on the remote sensing community to provide convincing
examples and illustrations of the digital use of airborne images in forest inventory
applications. To date, the common approach to remote sensing for forest inventory
is to apply an empirical classi®cation or estimation technique. The core of this
Incorporating texture into forest classi®cation
63
method involves establishing an empirical relationship between spectral measurements and ®eld estimates of inventory variables, such as species or density, and the
subsequent extension of these relationships to larger areas by a classi®cation decision
rule. Recent studies have focused on extracting information from the image prior to
developing classi®cation rules (Hay and Niemann 1994), or, using ancillary data
such as a digital elevation model (DEM) to develop decision rules for forest classi®cation (Franklin 1994). Several studies (Fournier et al. 1995, St-Onge and Cavayas
1995, 1997, Lark 1996), have reported that texture analysis can improve classi®cation
accuracy, but much work remains on how and where texture analysis can be e ective.
Texture derivatives (Roach and Fung 1994, Wulder et al. 1998) and texture segmentation methods (Lobo 1997, Ryherd and Woodcock 1997), for example, require further
development and testing in forestry and ecological mapping applications.
The objective of this study was to evaluate the use of two texture measures when
combined with spectral features in the classi®cation of forest structure as de®ned by
provincial forest inventory procedures. Of interest was to classify a range of stands
from pure coniferous and deciduous to mixed-wood stands in southern Alberta and
south-eastern New Brunswick from high spatial resolution airborne images. A range
of forest stands in di erent ecological conditions was selected to provide the widest
possible range of test conditions for this initial study. Texture information represents
the spatial variation in image tone (i.e., digital grey values) that is the result of the
arrangement of forest vegetation and other objects in a digital image. The variability
in stand structure results in unique variations of image tones that can be used to
stratify stands, and to possibly increase the accuracy of forest classi®cation and
mapping of biophysical attributes.
2.
Study area and data collection
The forests in this study are located adjacent to Kananaskis Country Provincial
Park in Alberta, and in the Fundy Model Forest in south-eastern New Brunswick
(®gure 1). The Alberta site is considered part of the Canadian Montane Forest
Region (Strong and Leggat 1981). The relatively well-drained lower slopes of alluvial
fans and lacustrine deposits are dominated by aspen (Populus tremuloides Michx.)
and the upper, dryer slopes are dominated by lodgepole pine (Pinus contorta Dougl.).
White spruce (Picea glauca (Moench) Voss) and balsam poplar (Populus balsamifera
L.) are usually found in stands dominated by pine and aspen, respectively.
The New Brunswick site is considered part of the Canadian Acadian Forest
Region (Rowe 1972). Northern tolerant hardwoods, sugar maple (Acer saccharum
Marsh.), yellow birch (Betula alleghaniensis Britton) and beech (Fagus grandifola
Ehrh.) are common on upper slopes and ridges. Conifer stands composed of balsam
®r (Abies balsamea (L.) Mill.), red spruce (Picea rubens Sarg.), white spruce and black
spruce (Picea mariana (Mill.) BSP) are most common on lower slopes, and mixedwood stands are frequently found on mid-slopes. Red maple (Acer rubrum L.),
trembling aspen, bigtooth aspen (Populus grandidentata Michx.) and white birch
(Betula papyri®ra Marsh.) are common in large part because of frequent harvesting.
Jack pine (Pinus banksiana Lamb.) frequently occurs on areas with sandy soils, and
both red pine (Pinus resinosa Ait.) and white pine (Pinus strobus L.) are found in
hardwood and mixed-wood stands. White ash (Fraxinus americana L.), eastern white
cedar (T huja occidentalis L.), red oak (Quercus rubra L.) and eastern hemlock (T suga
canadensis (L.) Carr.) are found in the region. Stands with numerous species in the
64
S. E. Franklin et al.
Figure 1. Location of study areas in boreal forests in south-western Alberta and southeastern New Brunswick.
overstory (four or more) are common in this region because of the large number of
tree species found in abundance.
2.1. Field data collection
Table 1 contains a listing of the ®eld data characterizing each of the 26 Alberta
plots, and table 2 contains a similar listing of the ®eld data characterizing each of
the 17 New Brunswick plots used in the analysis.
Field plots that were 10 mÖ 10 m in size were established in Alberta and
20 mÖ 20 m in New Brunswick to represent a range of conditions on near-level
terrain to minimize topographic e ects. Standard Alberta Vegetation Inventory (AVI)
methods (Alberta Forestry, Lands and Wildlife 1991), and New Brunswick Integrated
Land Classi®cation System (New Brunswick Department of Natural Resources 1996)
methods were applied to classify each plot established in the Alberta and New
Brunswick study areas, respectively. Estimates of crown closure in Alberta were
made at ®ve locations within each plot with the aid of a hand-held spherical
densiometer. Plot tree heights were taken for two dominant or co-dominant trees
with a hand-held clinometer. Diameter at breast height (DBH) was also measured
for all trees in a plot, and extensive notes about understory species were collected.
Field measurements of individual trees in each plot were used to generate a stand
descriptor label that included (Gillis and Leckie 1993):
1.
2.
3.
4.
Species (i.e., percent canopy for dominant and co-dominant trees)
Density (i.e., four or ®ve categories related to crown closure)
Height
Stand origin (corresponding to year of origin in Alberta) or age (corresponding
to young, immature, mature or overmature in New Brunswick) and
5. Other attributes such as understory, site quality and drainage.
Forest inventory classi®cations were generated in the ®eld and then compared to
Table 1. Summary of ®eld data for the 26 Alberta forest plots.
Species composition according to the AVI code
Plot ID
Middlestory
Merged
Pl5 Pb5
Aw10
Aw10
Pl10
Aw10
Aw10
Aw10
Aw10
Aw10
Aw10
Aw5 Pb4 Sw1
Aw9 Pl1
Pl10
Pl3 Aw3 Sw2 Pb2
Aw7 Sw3
Pb4 Aw3 Pl2 Sw1
Pl10
Sw5 Pl3 Aw3
Pl6 Sw4
Pl10
Pl4 Sw3 Aw2 Pl1
Pl5 Sw5
Pl6 Sw4
Pl8 Sw2
Pl8 Sw2
Pl10
Aw10
Aw10
Aw10
Aw10
Aw10
Aw10
Aw10
Aw10
Aw10
Aw10
Aw10
Aw4 Pb4 Sw2
Aw8 Pl2
Aw7 Pl3
Aw3 Pb3 Pl3 Sw1
Sw4 Aw4 Pb2
Pb4 Sw3 Aw2 Pl1
Aw5 Pl4 Sw1
Sw4 Pb4 Aw1 Pl1
Sw4 Pl3 Pb3
Pl7 Aw3
Sw4 Pl4 Pb1 Aw1
Sw5 Pl4 Aw1
Pl5 Sw5
Pl7 Sw3
Pl7 Sw3
Pl10
70
70
70
64
70
70
64
70
70
64
70
64
64
70
68
64
64
68
64
64
64
64
66
73
73
64
Aw10
Aw10
Pb5 Sw5
Aw5 Pl4 Pb1
Aw9 Pl1
Pb7 Sw3
Sw5 Pb5
Sw10
Aw6 Pl3 Sw1
Pb7 Sw3
Sw5 Pb5
Aw6 Pl4
Pl5 Sw3 Aw1 Pb1
Sw5 Pl3 Aw2
Sw10
Pl5 Sw5
Sw10
Pl6 Aw2 Pb2
Crown closure³
Percent
AVI code
46
49
36
54
64
53
36
52
55
51
54
60
50
65
59
60
61
63
54
52
63
60
30
50
50
38
B
B
B
C
C
C
B
C
C
C
C
C
B
C
C
C
C
C
C
C
C
C
A
B
B
B
²Species composition: Aw = Trembling Aspen, Pb = Balsam Poplar, Pl = Lodgepole Pine, Sw = White Spruce, 1= 10%, 2= 20%, ... 10= 100%.
³Crown closure: A= 7±30%, B= 31±50%, C= 51±70%, D= 71±100%.
§AVI Inventory polygon descriptions.
Polygon no.
64
66
Polygon no.
B11Pl10
A10 Pl7Sw2Aw1
B11Pll4Aw4Sw2
A6Pl5Aw4Sw1
B15Pl8Aw2
B10Pl7Aw2Sw1
70
73
AVI code*
overstory
middlestory
overstory
middlestory
*AVI code is de®ned as crown closure³, stand height to closest metre, and species composition².
C12Aw9Pl1
A4Sw8Aw2
C17Pl5Aw5
A10Sw8Pl2
65
68
overstory
middlestory
overstory
middlestory
overstory
middlestory
AVI code*
Incorporating texture into forest classi®cation
14
21
22
10
19
20
23
24
25
26
17
18
9
13
15
16
8
12
6
7
11
4
1
2
3
5
Overstory²
AVI inventory polygon
number§
66
S. E. Franklin et al.
Table 2. Summary of ®eld data for the 17 New Brunswick plots.
Species composition²
Density
(stems haÕ 1 )
LAI
New Brunswick inventory
polygon label³
HB1
HB3
HB4
HB5
HB6
HB7
HB8
HB9
HB10
jP1 0
rM6 bF2 jP2
rM4 rS3 wP2 jP1 wB1
wB8 rM2
rM5 wB4 Be1
rM5 wB2 rS2 bF1
rM5 wB5
rM3 bF3 wB2 wP2
jP6 rM2 gB1 tA1
5000
295
1350
1300
1275
525
625
625
1825
2.63
3.86
6.77
4.76
4.71
3.89
3.84
3.84
6.01
jP1 0
IH4 SP3 SW2
SP7 WP2
IH9
IH9
IH4 SP3 WP3
TH4 IH3 SW3
SP4 WP4
IH7 WP2
DU2
DU3
DU5
DU6
DU7
DU8
DU9
DU10
jP1 0
wS1 0
rM5 yB2 Be1 sM1 wB1
bF4 rM4 rS1 wB1
Be3 wB3 rM2 rS2
wB6 rM2 rS2
rM7 rS1 tA1 wB1
wB7 rM3
2500
1475
1675
1050
1000
1375
675
1275
5.61
5.83
4.93
5.87
6.88
5.25
3.28
4.30
jP1 0
SP1 0
IH6 TH4
IH4 TH4 SW2
TH1 0
IH6 TH3
IH6 TH3
IH6 TH3
Plot ID
²See ®gure 3 for a description of species composition labels.
³Species descriptors: IH= tA, wB, gB; TH= yB, Be, rM, sM; SW= mixed pine, spruce, ®r;
SP= wS, rS.
the GIS forest inventory database. In every case, the ®eld inventory was more
detailed than the existing forest inventory. This is attributed to the photo interpretation process that results in an average descriptor for the stand. For example, for the
26 plots surveyed in the ®eld in the Alberta site, only ®ve AVI polygons were mapped
in the GIS (table 1). The more detailed ®eld labelling was retained for this analysis
because of the requirement to compare the image classi®cation with its corresponding
stand type in the ®eld. The accuracy by which the image data could predict pixel
membership within the plots was then determined by comparison with the ®eld
measurements.
2.2. Airborne remote sensing data collection
The high spatial resolution data included airborne multispectral video images
acquired July 1996 in Alberta (Roberts 1995, Gerylo et al. 1997), and airborne
spectrographic images acquired August 1995 in New Brunswick (Anger et al. 1994,
Wulder et al. 1996). The spectral data in Alberta consisted of three image bands
from 490 to 850 nm. In New Brunswick, the spectral data included ®ve image bands
from 560 to 750 nm. Image acquisition for both study areas were accomplished by
a light aircraft ¯ying perpendicular to the major solar axis, and was restricted to
high solar angle times on days with little or no cloud cover. Atmospheric correction
procedures included a dark-pixel object subtraction routine, using the deep waters
of nearby lakes, and an empirical calibration using pseudo-invariant objects measured
with the ASD Personal Spectrometer II during each airborne mission (Milton
et al. 1997).
The highest spatial resolution images used in this study approached 0.3 m. In
Alberta, multispectral video (MSV) (Roberts 1995) images were obtained from three
Incorporating texture into forest classi®cation
67
co-registered, ®ltered and calibrated SONY XC-75 CCD (charge coupled device)
video cameras. Six short ¯ight lines were ¯own 200 m above the site, and at ®ve
increasing altitudes up to 3500 m, resulting in images with approximately 0.3, 1, 2,
4, 6 and 8 m spatial resolutions, respectively. In New Brunswick, images were
obtained from the compact airborne spectrographic imager (CASI) (Anger et al.
1994) over two 10-km long ¯ight lines, from an altitude of approximately 3000 m
that resulted in a spatial resolution of 1 m.
The sample plots were located on the geocoded images by a di erential GPS
ground survey and a GPS base station. Visual clues were also used to locate plots
when problems with overstory de¯ection of the radio signal (Ho man-Wellenhof
et al. 1992) were suspected. Plot locational accuracy was within 1 m of its true
location, and plots were generally restricted to near-nadir positions and digital image
frame centres to reduce bidirectional e ects, lens vignetting and radial displacement.
None of these potential sources of error appeared to compromise the image samples
used in the study when subjected to detailed image interpretation on computer
displays.
3.
Methods
3.1. Classi®cation hierarchy
The relationships between the image data and plot-level forest inventory were
obtained by image classi®cations using the maximum likelihood decision rule. The
discriminating power of the spectral and textural variables was determined by the
success of the classi®cation shown in contingency tables and per cent classi®cation
accuracy.
An analysis of accuracy was based on a series of classi®cation tests whereby the
results of pixel class membership were compiled for di erent levels of inventory class
generalization. Classes at each level of the hierarchy were either accepted, eliminated,
merged or modi®ed according to the structure of the forest inventory system, and
based on the maximum detail that could be retained in the pixel classi®cations for
that plot. This process provided insight into the use of texture as a selective variable
that is class speci®c (Hay and Niemann 1994, Ryherd and Woodcock 1997). The
®rst level of the classi®cation hierarchy assumed that each plot in the sample was a
separate and distinct class. The second level assumed that similar plots represented
the range of conditions in a single class described by a species composition label.
The third and fourth levels of the classi®cation hierarchy represented increasing
generalizations of the forest inventory description. In the New Brunswick sample,
the spectral classi®cation accuracy in each original plot was examined to determine
whether to include texture into classi®cation for that plot, or to accept the accuracy
attained without further discrimination.
In conventional classi®cation there is the assumption that every pixel in the plot
is a member of the class that it represents (Franklin 1994, Hay and Niemann 1994,
Fournier et al. 1995). This can be a source of pixel classi®cation error because many
pixels in any given plot will not uniquely belong to the class the plot is supposed to
represent. Earlier work (Franklin 1994) has shown that a composite spectral signature
consisting of many objects contributing to re¯ectance can be used to classify high
resolution image data with a reasonable (i.e., nonrandom) degree of accuracy. The
use of image texture in the hierarchical classi®cation of forest inventory plots should
provide additional discriminatory power because, while the spectral response is an
aggregate of the individual objects contributing to re¯ectance, the texture variable
68
S. E. Franklin et al.
represents the spatial pattern in which these objects occur. The hypothesis that
adding texture to spectral classi®cation will increase land cover map accuracy is
consistent with the use of texture as an element of photo interpretation in forest
inventory (Leckie et al. 1995). The classi®cation accuracy in this study is reported
as the percentage of correct pixels in the ®eld plot that were classi®ed into the class
from which they originated, as determined by the ®eld survey.
3.2. Image texture measures
Image texture is a quanti®cation of the spatial variation of image tone values
that are related to changes in the spatial distribution of forest vegetation, both in
the vertical and horizontal dimensions. Image texture is a complex concept and
application. Simple texture measures may be derived by comparing the values of the
digital numbers within a window, or in derivatives of a matrix representing the greylevel co-occurrence within the window (Haralick 1986, Oja and Valkealahti 1996).
Two co-occurrence texture derivatives described by Franklin and Peddle (1987) were
applied to the infrared channel (approximately 740±760 nm) to characterize the
texture of the vegetation for each plot. Only simple texture measures were derived
based on preliminary interpretations of image displays (Franklin and Peddle 1990)
to illustrate their use and the degree of improvement possible in plot classi®cation.
More complex multidimensional texture measures are possible, and subsequent study
may attempt to optimize the texture measures for this application.
The size of the window over which texture measures are derived may be altered
or adapted to better represent the characteristics of the local area (Haralick 1986,
Marceau et al. 1990, Ryherd and Woodcock 1997). Large windows (eg., 9 Ö 9 compared to 3 Ö 3) can provide better estimates of distributions and decrease the random
error, but may encompass more than one stand type that could introduce systematic
error. An increased ability to estimate forest stand parameters such as LAI, stand
density and volume have been reported after customized windows sizes were used
rather than ®xed, arbitrary windows (Franklin and McDermid 1993, Wulder et al.
1998). These are data-driven geographic windows, rather than arbitrary, ®xed geometric windows (Franklin et al. 1996). Image semivariance, based on image variograms,
was used to relate pixel self-similarity over a distance to suggest optimal geographic
window sizes based on the characteristics of a particular spectral bandset. In images
of lower spatial resolution with fewer pixels than the optimal window size predicted
by the semivariogram, the window was restricted to the plot area.
An inter-pixel sampling distance of one and a zero degree angle were utilized on
8-bit linearly transformed 16-bit data to calculate the texture derivatives from the
spatial co-occurrence matrix. Texture measures homogeneity and entropy were used
in this study (PCI Inc. 1994):
n
m
Homogeneity =
j -1 i-1
n
P(i,j )
(1 + [R (i )Õ C( j )]2 )
(1)
m
Entropy = P (i,j ) 1n (p(i,j )) if P(i,j )^0 otherwise, Ebtropy = 0
(2)
j -1 i-1
where P(i,j ) is the spatial co-occurrence matrix element, R (i ) is the grey level value
for a row, and C( j ) is the grey level value for a column.
69
Incorporating texture into forest classi®cation
4.
Results
4.1. AVI classes
Based on spectral and texture classi®cations of the images acquired from six
di erent altitudes in Alberta, the highest spatial resolution images were the least
accurate at each level of the hierarchy. These images improved the most, however,
when texture derivatives were added to the classi®cation decision rule (®gure 2,
table 3). In the ®rst level of the hierarchy over all the classes, spectral data alone
yielded just 15% accuracy at the highest level of spatial detail; this improved to 40%
with the higher altitude (lower spatial resolution) imagery. At the second and third
levels of the hierarchy, the same pattern was found: 23% to 34% accuracy, and then,
52% to 68% accuracy, respectively. These patterns con®rmed that the mean spectral
response measured in the plot did not produce an accurate classi®cation of the pixels
in that plot alone, but the addition of simple texture derivatives improved the overall
relationship between image data and ®eld observations.
When the six spatial resolutions for a given level of the classi®cation hierarchy
were averaged, the airborne images were 26% correct at the highest level of detail
in the classi®cation hierarchy (®gure 2). This accuracy was reduced to 22% when
only texture variables were used, and improved to 37% when both texture and
spectral variables were used in the decision rules. The second level of the hierarchy
showed a di erent pattern of 28% to 34% to 46% (®gure 2). The third level of the
hierarchy showed a similar pattern of 60% to 57% to 65%. At this level of classi®cation detail, the texture variables were more powerful than the spectral variables in
discriminating the inventory classes at four of the six spatial resolutions examined
in the study. The exceptions included the two images with the lowest spatial resolutions. On average, the spectral discrimination accuracy approximated or exceeded
the texture discrimination accuracy.
The range of accuracy was large for di erent levels of the hierarchy and for
di erent image resolutions. The highest accuracy found in the classi®cations was
produced by the texture data at the highest spatial resolution, but with the lowest
level of detail in the class hierarchy (97%, three classes). The lowest accuracy found
in the classi®cations was produced by the texture data at the lowest spatial resolution
but with the highest level of detail in the class hierarchy (10%, 26 classes).
Table 3. Summary of the classi®cation results for two dierent spatial resolution images and
the mean of all images at each level of the hierarchy over the Alberta plots.
Class structure²
Level 1
Level 2
Level 3
Spatial
resolution (m)
Spectral
data alone
Texture
data alone
Combined
0.3
8.0
mean³
0.3
8.0
mean
0.3
8.0
mean
15
40
26
23
34
28
52
68
60
23
10
22
32
17
34
76
41
57
31
44
37
44
40
46
74
64
65
²See ®gure 2 for a description of species composition labels.
³Mean of all six spatial resolutions at each level of the hierarchy shown in box at top of
®gure 2 for each set of plot results.
70
S. E. Franklin et al.
Figure 2. Spectral response, texture derivative, and combined spectral and texture hierarchical classi®cation for 26 Alberta (AVI) forest plots.
Incorporating texture into forest classi®cation
71
Individual class performance in the classi®cation varied greatly by spatial resolution and level of classi®cation hierarchy. A few notable patterns emerged, however,
which appeared to strengthen the rationale underlying the use of image texture in a
selective method, rather than application to every class and training sample. For
example, the aspen stand in plot 26 varied in accuracy from 68% at the lowest
spatial resolution in the ®rst level of the hierarchy, and was reduced in accuracy to
45% with the addition of texture variables. The original 68% accuracy attained is
considered acceptable, showing that this plot is spectrally distinct for image classi®cation purposes. Texture derivatives appeared to confuse this distinctiveness and
reduced the accuracy achieved with the spectral data. This ®nding was not consistent
with the satellite image texture segmentation results reported by Ryherd and
Woodcock (1997), where they found the combination of texture and spectral data
never degraded segmentation accuracies. Classi®ers may be more sensitive, however,
to texture variables than are segmentation routines. Plot 25, for example, contained
a similar stand of aspen with an increased density (table 1) that was improved in
accuracy from 26% to 77% with these same image data. This plot was more similar
to the overall pattern that was dominated by increased discrimination provided by
texture derivatives, particularly in the mixed-wood stands.
4.2. New Brunswick Department of Natural Resources (NBDNR) classes
A classi®cation hierarchy with a single image dataset was also constructed for
the New Brunswick study plots (®gure 3). Texture derivatives provided only marginal
increases in classi®cation accuracy relative to using spectral data alone. At the ®rst
level of the hierarchy, accuracy was 42% using spectral data alone, 23% using
texture, and 42% using the combination of spectral and texture data in the decision
rule (®gure 3). The addition of texture increased accuracy consistently across the
inventory classes for the middle two levels of the hierarchy, but the levels of accuracy
were still relatively low. At level four of the hierarchy, accuracy improved to 57%
with spectral and texture data. Only a small 2% increase in accuracy was achieved
in comparison to using spectral data alone.
The addition of texture measures to the classi®cation resulted in less discrimination among stands of similar species composition in comparison to use of only the
spectral data. The relative frequency and dominance of 12 hardwood stands relative
to ®ve softwood stands, and its variable composition may explain, in part, the results
attained (®gure 3). Hardwood stands were adequately classi®ed with the spectral
data and little additional discrimination was achieved with the addition of texture.
In this study, however, texture refers to the co-occurrence texture used in the study,
of which many formulations of texture were possible and could be useful to describing
the textural characteristics of a given land cover class.
A second classi®cation sequence was applied to the New Brunswick plots,
whereby texture was not applied to spectral classi®cations that achieved 60% accuracy, or to classes where texture made little or no improvement to class discrimination
(®gure 4). The overall classi®cation accuracy did not change signi®cantly with 54%
(®gure 4) compared to 57% (®gure 3), but more importantly, the number of classes
retained in the class structure equalled the original level of the hierarchy. When
compared to the original spectral classi®cation at level 1 of the hierarchy (42%), a
12% increase in accuracy was achieved by using texture selectively in comparison
to applying texture to all land cover classes.
The presence of texture variability was illustrated for a pure jack pine plantation
72
S. E. Franklin et al.
Figure 3. Spectral and texture hierarchical classi®cation for New Brunswick (NBDNR) forest
plots.
(®gure 5 (a), plot DU2) in comparison to a plot comprising jack pine, red maple,
grey birch and trembling aspen (®gure 5 (b), plot HB10). Plot DU2 (®gure 5 (a)) did
not increase in accuracy with the addition of texture and was adequately classi®ed
using spectral data alone (87%). This plot was a single species, closed canopy
Incorporating texture into forest classi®cation
73
Figure 4. Selective texture classi®cation for New Brunswick (NBDNRI) forest plots.
plantation jack pine that presented a very homogeneous spectral response to the
sensor. Consequently, the mean spectral response and standard deviation were
suciently unique (compared to other classes in the sample) that a relatively high
classi®cation accuracy could be achieved. Plot HB10 (®gure 5 (b)) increased in
accuracy with the addition of texture from a low of 15% using spectral data, to a
74
S. E. Franklin et al.
(a)
(b)
Incorporating texture into forest classi®cation
75
maximum of 45% using spectral plus texture data in the decision rule (®gure 4).
This plot contained a mixture of jack pine and at least three di erent deciduous
species in a closed canopy. Visually this plot was inherently `textural’ and although
the improvement in classi®cation accuracy was impressive, the relatively low accuracy
even after adding texture suggests alternative texture measures may be required to
discriminate this inventory class (Roach and Fung 1994).
4.3. Discussion
The highest overall classi®cation accuracies reported in these analyses of multispectral, high spatial resolution, airborne videographic and spectrographic images
in Alberta and New Brunswick were 65% and 57%, respectively. These results were
derived from classi®cation of spectral and texture features, and were compared to
®eld plots inventoried according to their prevailing provincial systems. The spectral
classes were highly detailed and similar to the species composition labels used in
forest inventory operations. The accuracies achieved were likely comparable to those
that were obtained in the present GIS-based, forest inventory derived primarily
through aerial photo mapping.
The classi®cation accuracy results obtained with this study were similar to other
studies that have incorporated texture analysis in land cover mapping (Franklin and
Peddle 1990, Ryherd and Woodcock 1997). Texture processing generally improves
classi®cation accuracy in the order of 10±15%. There were few comparable applications of airborne image texture analysis in forest inventory studies. Earlier work by
Franklin and McDermid (1993) reported the best overall classi®cation accuracy
based on airborne spectrographic images and texture derivatives was 63% in seven
stand volume classes of Alberta lodgepole pine stands. Similar classi®cation methods
and accuracy assessment procedures were used, and the original classi®cation
accuracy using only three-band spectral data was 46%. Additional customized multiband texture and ®lter processing of the image data yielded 75% classi®cation
accuracy. St-Onge and Cavayas (1997) reported error levels of approximately 20%
for forest cover and density classes, but their approach incorporated texture and
image segmentation in a more advanced image processing methodology (Lobo 1997).
Similarly, Gougeon (1995, 1997) has described the results of classi®cation procedures,
based on individual tree recognition algorithms, that has generated accuracies in the
range of 68% in primarily pure conifer stands.
In each of these studies, comparisons were made between digital image processing
results and ®eld data or air photo interpretations that were considered 100% correct.
A more reasonable perspective is to assume that error exists in both ®eld and remote
sensing surveys, and that both datasets can lead to a reasonable generalization of
the forest inventory. Direct comparisons of air photo interpretation and ®eld inventory assessments have also yielded similar error rates (Biging and Congalton 1991,
Figure 5. Example infrared image data in two New Brunswick forest plots. The green colour
shades represent conifer species, and the pink to red shades represent deciduous
species. (a) Plot DU2Ða homogeneous jack pine plantation, closed canopy, high
classi®cation accuracy (87%) based on spectral data alone. (b) Plot HB10Ða complex
mixture of jack pine and three deciduous species that appears inherently textural,
closed canopy, low classi®cation accuracy based on spectral data alone (15%) but
increased accuracy when texture derivatives were added (45%).
76
S. E. Franklin et al.
Fent et al. 1995, Hall and Fent 1996) to those reported from digital image processing studies.
All of the above comparable studies incorporate texture into the analysis and all
have achieved results that are unambiguous in con®rming the validity of the concept
of digital image texture. The fact that digital image data can be classi®ed with an
accuracy and con®dence level consistent with existing methods of forest inventory,
however, does not imply stands can be mapped well (Franklin 1994). Clearly, di erent
and ¯exible strategies are required for the incorporation of texture variables, as
presented in this summary of the Alberta and New Brunswick airborne image
classi®cation results, and in the reporting of these results to the literature.
A checklist of the obvious sources of error or uncertainty in the application of
digital image texture to forest inventory classi®cation can be identi®ed and include:
1. The choice of the texture measure itself (Roach and Fung 1994, Lark 1996,
Wulder et al. 1998)
2. The area or window size for the texture calculation (Hay and Niemann 1994,
Marceau et al. 1990)
3. The way in which the texture measure is used in the analysis (i.e., classi®cation,
preprocessing, segmentation, combination of these, other) (Fournier et al. 1995,
St-Onge and Cavayas 1995, Lobo 1997)
4. The type and range of inventory classes that are considered (Leckie et al.
1995, Ryherd and Woodcock 1997, St Onge and Cavayas 1997)
5. The way in which the comparison to ®eld data is made (i.e., classi®cation
accuracy, regression, other) (Franklin and McDermid 1993, Baulies and Pons
1995, Fent et al. 1995)
6. The type of image data available, or the way in which the image acquisition
characteristics (i.e., spatial resolution, spectral bands, ancillary information,
and so on) are matched to the forest conditions under consideration (Milton
et al. 1997, Wulder et al. 1996)
7. The positioning accuracy of a plot and the plot size especially in relation to
image spatial resolution, and
8. The ultimate purpose of the classi®cation and the subsequent level of accuracy
required (i.e., the interpretive reason to produce maps based on digital airborne
remote sensing data and methods, or the accuracy levels required by the GIS
database standards employed, or the context of the classi®cation such as
update, inventory and exploratory mapping).
5.
Conclusion
Most studies in forest classi®cation that have utilized spectral and texture features
have tended to apply these features to all land cover classes. The selective application
of texture within a hierarchical classi®cation framework, however, resulted in higher
overall accuracies than application to all land cover classes. The highest classi®cation
accuracies achieved were in the range of 60±65% for pure and mixed-wood inventory
classes in two widely variable forests from Alberta and New Brunswick. These stands
were organized by species composition in di erent levels of generalization based on
detailed ®eld surveys which were more complete than existing GIS-based inventory
systems. For the two study areas, the addition of texture generally improved classi®cation accuracy for hardwood stands more so than for softwood stands. This result
was di erent than what has been previously reported because the use of texture
Incorporating texture into forest classi®cation
77
generally improves classi®cation of softwood stands due to their characteristic structure and shadows. Future studies will further examine the in¯uence of stand structure
and other inventory characteristics on image texture derivatives and their impact on
classi®cation accuracy. Although the results in this paper were consistent with those
reported in other Canadian studies and elsewhere using similar data and methods,
a more judicious use of texture appears justi®ed. This study is part of a larger
programme directed at deriving a greater understanding of the role high spatial
resolution, multispectral airborne images and classi®cation methods may have in
forest inventory classi®cation.
Acknowledgments
This research was supported by the Natural Science and Engineering Research
Council (NSERC) and the Canadian Forest Service. The Alberta image data were
provided by Dr Arthur Roberts (Department of Geography, Simon Fraser
University). The Alberta image database used in this study was created by Graham
Gerylo and is gratefully acknowledged.
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