DETECTION AND CLASSIFICATION OF NORWAY SPRUCE ...

IAWA Journal, Vol. 30 (1), 2009: 59–70

Detection and Classification of Norway Spruce Compression Wood in Reflected Light by Means of Hyperspectral Image Analysis Philipp Duncker* and Heinrich Spiecker Institute of Forest Growth, Albert-Ludwigs-University Freiburg, Tennenbacherstrasse 4, 79085 Freiburg i. Br., Germany *[email protected]

Summary

A methodology has been developed based on reflected light to detect compression wood in stem cross sections of Norway spruce (Picea abies [L.] Karst.). In addition to quantify the spatial distribution of compression wood, the chronological pattern of its formation is recorded by cross linking the pixel classification to the tree ring sequence. An imaging spectrometer is used to record the spectral characteristics in the visible light and near infrared of the cross-sectional surface. Cross-sectional areas are classified by hyperspectral image analysis into severe compression wood, moderate compression wood, normal wood, and background /cracks. The classification is performed by the Spectral Angle Mapper algorithm, which compares the standardized spectrum of each pixel with reference spectra stored in a spectral library. The reference spectra are obtained from selected training areas of the different compression wood severity classes identified by cell characteristics under a light microscope. The tree ring boundaries are located in a grey scale image which shows the spatial information at wavelength 435 nm and the annual radial increment is measured. The classification accuracy is tested by a confusion matrix and cross-analysed with High-Frequency Densitometry. Key words: Compression wood detection, hyperspectral image analysis, spatial and chronological distribution patterns, high-frequency densitometry. Introduction

Different studies have shown that formation of compression wood is a negative gravitropic reaction of the secondary meristem in coniferous trees (Westing 1965; Timell 1986b). It is interpreted as serving the tree’s recovery from a displacement in order to regain its original orientation in the field of gravity (Hartmann 1942; Riech & Ching 1970). Therefore, compression wood formation can be considered as a reaction to an external stress putting a strain on the tree. Information on cross-sectional and axial compression wood distribution can lead to a better understanding of the interaction of ecological factors and the associated growth responses of trees. Associate Editor: Thomas Yanosky

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IAWA Journal, Vol. 30 (1), 2009

The assessment of compression wood distribution within stems and branches demands an objective, repeatable detection method. The purpose of this study is to develop a methodology to examine the spatial, as well as the chronological, distribution pattern of compression wood on stem cross sections of Norway spruce. Background of compression wood detection Compression wood in cross sections is identified at the cellular level by its well known anatomical features (cf. Timell 1986a for a comprehensive overview). Yumoto et al. (1983) present a grading system for compression wood tracheid severity classes that allows for reliable classification by a trained operator. Automatic image analysis based detection at the cellular level by Fourier methods performs only well for severe compression wood (Moëll & Fujita 2004). Alternatively, compression wood might be detected on the basis of wood density (Rothe 1930; Ollinmaa 1961) since it has a higher density than normal wood (Cieslar 1896; Hartig 1896, 1901; Donaldson et al. 2004). Accordingly, intra-annual density patterns, including moderate or severe compression wood, should differ from those consisting solely of normal wood and might offer potential for compression wood detection. Both detection approaches require high surface preparation quality for recording microscopic images or wood density analysis at high spatial resolution, e.g. by means of High-Frequency Densitometry (Schinker et al. 2003). On a macroscopic level, colour is an apparent characteristic feature of compression wood and has often been used to differentiate it from other wood tissues. However, the reddish appearance tends to fade during drying of the wood, as already described by Mer (1888). Several suggestions were made to increase the apparent divergence between compression wood and normal wood. Contrast is enhanced through a green (Low 1964) or a blue filter in panchromatic light (Westing 1965). Timell (1986a) proposes the use of a moderately sensitive black and white film slightly overexposed to improve the contrast. Nyström and Kline (2000) conclude that colour scanning with a RGB camera works well for detecting compression wood in the fresh state but is less accurate after drying. These findings point to certain weaknesses in detection accuracy by three band colour value analysis. In consequence, an operator has to define reference regions for classification in each individual image (Wernsdörfer et al. 2004). It is the strength of hyperspectral image analysis, by enabling the detection of minor differences of light intensity in numerous wavelength bands, to overcome these difficulties and to offer more sophisticated classification algorithms. Hagman (1996) discussed the suitability of an imaging spectrometer as a sensor to evaluate soft wood quality moulding features on longitudinal and transverse faces of a piece of dried lumber. Hagman (1997) and Nyström and Hagman (1999) also presented an approach using hyperspectral image analysis in real-time compression wood detection on the longitudinal face of sawn timber. Reflected light is used to classify compression wood by its spectral properties. These properties are given by the chemical composition and the scene geometry of the hyperspectral scanner built-up for analysis. In this study, the obtained spectra from stem cross sections can be compared with those obtained from reference areas which have been identified by their cell structure under light micros-

Duncker & Spiecker — Detection of compression wood

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copy. Thus, hyperspectral image analysis might prove to be a useful methodology for detecting compression wood in dried and polished cross sections of Norway spruce without the need of analyst interaction. In addition to recording the spatial compression wood distribution, the annual radial increment can be measured since the growth ring boundaries are located as well. Finally, after dating, a chronological pattern of compression wood formation can be assessed. Materials and Methods

The principles of the hyperspectral scanner and acquisition of hyperspectral image datasets Reflected light from stem cross section surface is recorded with an imaging spectrograph. This is an instrument capable of simultaneously measuring optical spectrum components and spatial location of an object surface (Spectral Imaging Ltd. 2001). A complete spectrum is obtained for every pixel of an image across a two-dimensional surface matrix constituting a four-dimensional information space (spatial X-, Y-coordinates, wavelength and intensity). The operation principle is described by Hyvarinen et al. (1998), Spectral Imaging Ltd. (2001) and Hoyer and Malki (2003). A stem cross section is positioned with its pith centred in the rotation axis of a combined XY positioning and turning table directly under the spectrograph in the middle of the scanning line for image acquisition. After image acquisition the table returns to this starting position. Consequently, the cross section only needs to be turned to the desired angle for scanning the next radius. The Imaging spectrograph “ImSpector V10” (Specim Oy, Spectral Imaging FIN) in connection with a Canon objective (VF50 1.8 50 mm) and a digital camera (Teli 8310CS B/W 2/3” 10 bit) form the core of the system assembled for this study. The specification of ImSpector is given in Table 1. A halogen lamp (150 W, 21 V EKE), Table 1. ImSpector specifications and optical characteristics. Spectral range

400–1000 nm ± 5 nm

Image size

6.6 × 8.8 mm (2/3" CCD)

Spectral resolution Spatial resolution* Aberrations

Numerical aperture Slit width

Effective slit length PGP efficiency

5 nm (2.7 nm FWHM nominal) (leading to 121 bands) 0.1 × 0.079 mm

Bending of spectral lines across spatial axis < ± 2 nm 0.18 (F/2.8) 25 µm

8.8 mm

> 50%, independent of polarisation

*Depends on front optics and sensor resolution

An order blocking filter is placed in front of the camera sensor to prevent second order spectrum of the lower wavelengths from being diffracted to the same angle range as the first order spectrum of higher wavelengths range because this could lead to an overlapping situation on the sensor.

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with constantly controlled light temperature, and randomly oriented fibres of an optic light line illuminates the stem cross section in a bright uniformly distributed line of 10 cm in width. In order to avoid mirrored radiation in the sensor, the illumination angle is set to 45°. The distance between the spectrograph to the object is controlled by a laser distance measuring device and continuously adjusted. The distance determines the spatial resolution, which is chosen to be exactly a hundred pixels per square millimetre within the scanning image line. Further, the distance of the light source is adjusted too, in order to retain the illumination maximum in the scanning image line. Black and white references are recorded for every individual column in the image. The black reference is obtained by image acquisition with a shut objective and corresponds to the dark current of the camera which is considered to be noise. The white reference is an image frame of a diffuse reflectance standard acquired with the same exposure time as used while scanning. The black and white references are stored in the first two frames of the four-dimensional hyperspectral image data set acquired while scanning. The information contained in a resulting image cube can be visualised by extracting different cuts, e.g. an image giving the spatial information at a specific wavelength as shown in the upper half of Figure 1 or an image containing spatial and spectral information as indicated in Figure 2.

Figure 1. Cross section of a Norway spruce test radius at wavelength 435 nm (upper part of the image) and classification result in a grey-scale image: “Compression Wood severe” – white, “Compression Wood moderate” – grey, “Normal Wood” and “Background /Crack” – black.


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Figure 2. Spectral information and image of a cross section. The upper half shows spectral information of the wavelength range from 400–1000 nm along the middle line of the Norway spruce test radius from Figure 1 presented in the lower half of the image. Brighter values in the upper half correspond to higher light intensity within the different wavelength bands.

Image processing for the classification of compression wood and the measurement of annual radial increment The identification and classification of compression wood severity classes by its spectral properties is performed with an image processing batch routine written in software IDL 5.6 (/ENVI 3.6). The routine automatically processes the following steps for every image data file in a folder. The hyperspectral dataset is standardized by calculating the sample reflectance (R ci) at wavelengths (i) and spatial image columns (c). From the spectrum of every pixel the black reference of the corresponding column is subtracted and this difference is divided by the difference between the white and the dark reference: sampleci – black ci R ci = ––––––––––––––– . white ci – black ci Thus, the noise is reduced by removing the offset due to the dark current of the camera. Light source colour temperature drift and spatial non-uniformity of the light across the scene line are compensated for. In addition, comparability of different datasets is achieved, which enables classification according to uniform fixed references. The classification of the pixels in different wood tissue classes is done by comparing standardised spectra (range: 400–1000 nm; 121 spectral bands) with reference spectra for the different tissue types stored in a spectral library. The “Spectral Angle Mapper (SAM)” algorithm is applied for this supervised classification. It determines spectral similarity by calculating the angle between two spectra which are treated as vectors in space with dimensionality equal to their number of bands (Kruse et al. 1993). In this study, SAM compares the angle between the reference spectra vectors and each pixel vector in 121-dimensional space. Smaller angles represent closer matches to the reference spectrum.

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Table 2. Criteria of tracheid classification on cross sections.

Criteria of gradation of compression wood tracheids to normal tracheids in the middle of tree rings modified according to Yumoto et al. (1983). Criterion (cellular level)

Compression wood Compression wood severe moderate

Normal wood

Spiral grooves

distinct

poorly developed to absent

absent

Cell outline between S1 and S2 (L)

round, almost circular

round but variable depending on the presence of intercellular spaces

“rounded rectangles”

Cell wall thickness

Intercellular spaces Colour appearance

very thick

generally present

“reddish”

thicker than normal

incidentally present “darker brownish”

normal

absent

“brownish”

The reference spectra for the different wood tissue classes are obtained from a number of stem cross sections of individual Norway spruce trees. After preparation with an ultra precise diamond flight cutter (Spiecker et al. 2000) the cell structure of the cross section is clearly visible. Based on criteria at the cellular level, regions of interest have been selected as representative for the wood tissue classes “Normal Wood (NW)”, “Compression Wood moderate (CWm)”, and “Compression Wood severe (CWs)”. These regions are delineated in a superimposed polar coordination system centred in the pith. The criteria for gradation from compression wood tracheids to normal tracheids are chosen according to Yumoto et al. (1983), but slightly modified (Table 2). In addition to these classes, further classes for cracks, pith, bark, resin pockets, and differently illuminated background are defined. Mean standardised reference spectra are calculated for the reference areas of the classes. Spectral separability between the mean spectra of the different classes is confirmed by the Jeffries-Matusita and Transformed Divergence measures (Richards & Xiuping 1999) with test values > 1.892 (∈ [0, 2] with 0 = not separable, 2 = no interference between pairs, see Table 3. Figure 3 gives examples of standardised reference spectra. Since a total of 71 reference spectra are stored in the spectral library leading to 71 possibilities to classify a pixel, a final processing step is needed. It combines the different classes into four classes “Compression Wood severe (CWs)”, “Compression Wood moderate (CWm)”, “Normal Wood (NW)”, and “Background/Crack”. The interim Table 3. Spectral separability of reference classes. pair of reference classes

latewood and “CWs” bark and latewood bark and “CWs” pith and bark pith and “CWs” all other pairs

Jeffries-Matusita, Transformed Divergence*

1.89151900 1.99003559 1.99790343 1.99999941 2.00000000 2.00000000

“CWs”: severe compression wood; * ∈ [0, 2] with 0 = not separable, 2 = no interference between pairs.


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Examples of standardised reference spectra

Reflectance (1/1000)

800 compr wood sev.

700

compr wood mod.

600

NW – earlywood

500

NW – latewood

400

background / bright

300

background / dark

200

pith

100 0 400

bark

500

600

700

800

900

1000

Wavelength (nm)

Figure 3. Examples of standardised reference spectra obtained from microscopically identified areas for different classes of interest. Abbreviations: compr wood sev. (severe compression wood), compr wood mod. (moderate compression wood), NW (normal wood).

subclasses, comprising both early- and latewood spectra, account for the tremendous spectral differences in class “NW”. The classification accuracy can be increased further by applying a spatial filter, e.g. “Majority Analysis”, to the classification image which homogenises spurious pixels within a single class (Jensen 1996; ENVI 2001). The classification image gives the surface area of the identified classes. Their spatial distribution is described by the coordinates of the corresponding pixels. Linking the pixels to different tree rings results in a chronological pattern of class distribution. The tree ring boundaries are automatically identified in a grey scale image which shows the spatial information of the radius. A task solved for conifer species with different image analysis algorithms (Thetford et al. 1991; Guay et al. 1992; Conner et al. 2000; Soille & Mission 2001). Our investigations have revealed that the high contrast in the blue range of the spectrum between earlywood and latewood facilitates the location of the tree ring boundaries. The boundaries are detected by the sharp difference in brightness between the dark latewood and next year’s bright earlywood in the wavelength band 435 nm (e.g. Figure 1) in a kernel along the middle line. Test of classification accuracy and validation of classification result by cross-analysis with high-frequency densitometry A confusion matrix is calculated to assess the accuracy of the hyperspectral classification result. This is to compare the classification result of a test radius (Figure 1), which is not used for building the spectral library, with microscopically identified (truth) regions for the various classes. The truth regions are identified as described previously for obtaining reference spectra. As a measure of agreement between the result of the hyperspectral and microscopic classification the confusion matrix displays the overall accuracy which is the percentage of correctly classified pixels. The Cohen’s kappa coef-

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ficient indicates how accurate the classification output is beyond chance. Further, the producer and user accuracy are given, which are the percentage of a particular truth class correctly classified and respectively the percentage of a class corresponding to the assigned truth class. Finally, errors of commission and omission which are pixels incorrectly assigned to or respectively excluded from a particular class are provided (Lillesand & Kiefer 2000). For cross-analysis with intra-annual wood density patterns four stem cross sections are taken from different Norway spruce trees. Their surfaces are prepared with an ultra-precise diamond flight cutter (Spiecker et al. 2000). Along eight radii in each cross section, intra annual density patterns are assessed with High-Frequency Densitometry using a probe of 590 µm slit length and 34 µm slit width (Schinker et al. 2003). Excluding the innermost growth rings with the pith, 1792 patterns are recorded with a spatial resolution of one density measurement every 0.0122 mm. The intra-annual density patterns are standardised to relative intra-annual positions and a relative intraannual density range. This is done to account for different growth ring widths and for different mean density variations between trees. The standardised patterns are grouped according to the hyperspectral classification result (CWs, CWm and NW) into three groups: “Normal wood”: (CWs+CWm) < 5 pixel at growth ring “CompWoodmod”: 50% > (CWs+CWm) > 5 pixel and CWs < CWm “CompWoodsev”: CWs ≥ CWm > 5 pixel or (CWs+CWm) > 50%

Within each group the mean intra-annual density pattern and its corresponding 99% confidence intervals are calculated. Thus, they can be compared for significant differences. RESULTS and Discussion

Classification accuracy of the methodology The classification accuracy of the method developed is assessed by scanning a randomly selected test radius not used for building up the spectral library. The upper half of Figure 1 shows the spatial information at wavelength 435 nm. The pith, bright parts of earlywood followed by darker latewood, as well as two cracks are readily visible. In addition, compression wood can be recognised as crescentiform areas. In the lower half of Figure 1 the classification result is given in a grey-scale image. A confusion matrix for microscopically identified test areas is calculated (data not shown) and reveals an overall accuracy of > 91% and a Kappa coefficient of ~0.80. CWs is detected with a producer and user accuracy of 55.63% and 70.02%, CWm with 72.23% and 88.47%, and NW with 99.78% and 97.78%, respectively. Producer accuracy and error of omission sum up to 100% as well as user accuracy and error of commission. Figure 1 shows that some errors in automatic detection occurred in the pith and on the inner side walls of the two cracks where pixels are classified as belonging either to CWs or CWm. Such misclassifications can be corrected manually in the data files. All unclassified pixels are located outside the cross section in the dark background where the signal was judged to be too weak. This observation would allow for the combina-


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tion of the two classes “Background/Crack” and “Unclassified” into a single class, improving the overall accuracy to 96%. Furthermore, it can be observed that compression wood, irrespective of its gradation, is hardly ever located in the very earlywood of a tree ring. The compression wood occurs as more or less cohesive arcs which are interspersed with small areas of normal wood. This is partly in accordance with our observations of compression wood anatomy, especially the moderate forms, which are not absolutely homogeneous but rather a mixture of small areas with cells of different compression wood gradation. Relative intra-annual density (%)

100 90 80 70 60

Comp Wood sev

50

Comp Wood mod

40 30

Normal wood

20 10 0

0

10

20

30

40

50

60

70

80

90

100

Relative intra-annual position (%)

Figure 4. Mean standardised intra-annual density profiles and the 99 % confidence interval. “CompWoodsev”: severe compression wood, “CompWoodmod”: moderate compression wood and “Normal wood”: normal wood.

Cross-analysis of classification result with high-frequency densitometry The 1792 standardised intra-annual density profiles obtained in total by HighFrequency Densitometry are grouped according to the hyperspectral classification result (1209 profiles belonging to group “Normal wood”, 472 to “CompWood mod” and 111 to “CompWood sev”). The mean densities and their confidence intervals for relative intra-annual positions of 1% are given in Figure 4. The three curves do not differ significantly at the very beginning of the intra-annual sections. From relative position 4% until position 77% mean density of group “CompWoodsev” is significantly higher than those of the other groups. At position 77% both compression wood groups have similar density. Group “CompWood mod” differs significantly from mean density of “Normal wood” from position 7% onwards. The mean density of “Normal wood” achieves density levels higher than 50% only at position 80%. Again no significant difference in density between the groups was observed from position 90% onwards. The relative mean density profiles are in accordance with expectations since compression wood in Norway spruce has a higher density than normal wood (Cieslar 1896; Hartig 1896, 1901; Rothe 1930; Ollinmaa 1961). Further, the profiles show that the

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areas classified by hyperspectral analysis as severe compression wood have an even higher mean density than those areas that mostly include moderate forms of compression wood. Thus, the findings of Donaldson et al. (2004) that severe compression wood has a higher density throughout the whole intra-annual section of a growth ring than normal wood for Pinus radiata is supported here for Picea abies, except for the latewood section. The cross analysis with High-Frequency Densitometry confirms the classification result of hyperspectral image analysis to detect compression wood in reflected light. CONCLUSION

Hyperspectral image analysis enables to discriminate “moderate” and “severe” compression wood from other wood tissues on the surface of dried and polished stem cross sections of Norway spruce in reflected light with an overall accuracy of at least 91%. The spectral differences permit to use fixed reference spectra for supervised classification. This facilitates to adjust the methodology to new tree species or additional classes if desired. Thus, the methodology becomes repeatable and can be performed automatically. The weak accuracy in compression wood detection through three band colour value (RGB) analysis is improved due to the application of hyperspectral image analysis. The algorithm used for classification makes use of multiple bands and the full spectral power. With a scanning speed of 1.25 mm/sec the method is designed for routine laboratory analysis of stem cross sections. All data and information needed to obtain a profound knowledge of compression wood distribution, thereby leading to a better understanding of the interaction of ecological factors and the tree growth reaction are provided by the presented methodology. References Cieslar, A. 1896. Das Rothholz der Fichte. Centralblatt für das gesamte Forstwesen 22: 149–165. Conner, S.W, R. A. Schowengerdt, M. Munro & M. K. Hughes. 2000. Engineering design of an image acquisition and analysis system for dendrochronology. Optical Engineering 39: 453– 463. Donaldson, L. A., J. Grace & G. M. Downes. 2004. Within-tree variation in anatomical properties of compression wood in radiata pine. IAWA J. 25: 253–271. ENVI 2001. ENVI User’s Guide. September, 2001 Edition. Research Systems, Boulder. 948 pp. Guay, R., R. Gagnon & H. Morin. 1992. A new automatic and interactive tree ring measurement system based on a line scan camera. The Forestry Chronicle 68: 138–141. Hagman, O. 1996. On reflections of wood. Wood quality features modelled by means of multivariate image projections to latent structures in multispectral images. Division of Wood Technology, Luleå University of Technology, Skellefteå, Sweden. 281 pp. Hagman, O. 1997. Multivariate prediction of surface features using an imaging spectrograph. Holz als Roh- und Werkstoff 55: 377–382. Hartig, R. 1896. Das Rothholz der Fichte. Forstlich-naturwissenschaftliche Zeitschrift 5: 96–109. Hartig, R. 1901. Holzuntersuchungen. Altes und Neues. Springer-Verlag, Berlin. 99 pp. Hartmann, F. 1942. Das statische Wuchsgesetz bei Nadel- und Laubbäumen. Neue Erkenntnis über Ursache, Gesetzmäßigkeit und Sinn des Reaktionsholzes. Springer-Verlag, Wien. 111 pp.


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