Assessment of forest damage with landsat TM: Correction for varying forest stand characteristics

REMOTE SENS. ENVIRON. 47:291-302 (1994)

Assessment of Forest Damage with Landsat TM: Correction for Varying Forest Stand Characteristics Sam Ekstrand* T h e utility of satellite data in forest decline assessment is influenced by effects associated with variations in stand characteristics such as species composition, age, and density. In this study, the spectral effects of stand variations were determined for forest dominated by Norway spruce. The usefulness of digitized stemd data for potentially reducing these effects were in ~estigated. It is suggested that a damage estimation algorithm based solely on Landsat TM Band 4 is more appropriate than earlier proposed ratio algorithms in areas with moderate defoliation symptoms but no chlorosis. The two factors with the strongest negative effect on the defoliation assessment were varying hardwood and pine component. A hardwood component of 20% completely neutralized a defoliation of 20%. Age had a clear spectral effect up to 70 years, but above that the response was stable. There was no confusion between defoliation classes in stands with moderate to high density. A spruce defoliation model that used stand data from digitized forest maps to modify the intensity values of TM Band 4 prior to estimation of defoliation was developed. The resulting assessment of moderate defoliation in forest areas on level ground was of adequate accuracy when the spruce component was larger than 75 %.

INTRODUCTION Norway spruce (Picea abies) is the most abundant tree species in the Nordic countries and in parts of central Europe. The species is severely affected by anthropo-

* Swedish Environmental Research Institute, Stockholm Address correspondence Sam Ekstrand, Swedish Environmental Research Institute, Box 21060, S-100 31 Stockholm, Sweden. Received 10 November 1992, revised 3 April 1993. 0034-4257 / 94 / $7.00 ©Elsevier Science Inc., 1994 655 Avenue of the Americas, New York, NY 10010

genic activities, causing what is generally referred to as forest decline or forest dieback. In Sweden, the effects are light to moderate with the main visible symptom being defoliation in the upper half of the crown. The most affected compartments generally have a mean defoliation of 35-40 %. Forest decline assessment has been the subject of numerous European and American remote sensing studies. Color infrared airborne photography has frequently been used in operative inventories in northern and central Europe (Hartmann and Uebel, 1986; Riom, 1987; Wastenson et al., 1987), and satellite studies have suggested that Landsat TM is capable of mapping severe forest decline (Ciolkosz and ZawilaNiedzwiecki, 1990; Rosengren and Ekstrand, 1987; Vogelmann, 1990). However, the capability to detect slight or moderate forest decline with satellite data has been a subject for discussion. This is because the spectral contribution of defoliation at moderate levels is subtle and very sensitive to changes in the stand characteristics and to terrain variations (Ekstrand, 1990; Koch et al., 1990; Leckie, 1987; Westman and Price, 1988). The purpose of this study is to 1) evaluate the spectral contributions of spruce defoliation and of variations in a number of stand parameters, 2) correct for stand structure effects prior to defoliation assessment, and 3) determine the assessment accuracy derived in operative inventories employing digitized forest maps and Landsat TM data. BACKGROUND Forest Damage Several authors have studied the spectral effects of forest damage. Generally, the effects differ considerably among the three main spectral domains of interest, that is, the visible (400-700 nm), the near-infrared (NIR, 700-1300 nm), and the short-wave infrared (SWlR, 1300-2500 nm).

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In the visible domain, most of the incoming radiation is absorbed by leaf pigments such as chlorophyll, xantophyll, carotenoids, and anthocyanines (Guyot et al., 1989). The effect of a decreasing chlorophyll content (chlorosis) is therefore strong. Only around 10% of the radiation in the visible is reflected from needles. This implies that when some of the needles fall off and the shadowed parts grow, the decrease in reflectance will be comparatively low. The effect may also be masked by an increasing amount of bark observed by the sensor. It seems probable that a moderate needle loss would result in a weak reflectance decrease, while severe needle loss could yield an unaltered or increased reflectance due to an increased bark spectral contribution, at least in dense canopies. If chlorosis occurs, the shadowing effect will most certainly be overruled in the blue and red parts, resulting in a reflectance increase in those parts of the spectrum. The spectral responses of damage symptoms observed so far support these theoretical considerations. Defoliation on Norway spruce and Scots pine results in a slight reflectance decrease (Ekstrand, 1990; Koch et al., 1990), although Rock et al. (1988) found that increased bark spectral contribution, an effect of severe defoliation, caused an increase in visible reflectance. Koch et al. (1990) and Guyot (1983) found that there was no change of the reflection characteristic for needle samples from trees with different degrees of defoliation but no chlorosis. The chlorosis (yellowing) effect is an increase in the blue part of the visible region, a decrease in the green, and a pronounced increase in the red part, observed by several authors (Hoque et al., 1988; Koch et al., 1990; Rock et al., 1986). A number of satellite, airborne, and field-based studies have shown that the near-infrared (NIR) reflectance decreases with an elevated defoliation level (Koch and Kritikos, 1984; Koch et al., 1990; Rock et al., 1988; Teillet et al., 1985). Koch et al. (1990) attributed this effect to increasing parts of shadow and dark background within the tree crown or stand. Teillet et al. (1985) and Rock et al. (1988) found that increasing the fraction of bark also results in a NIR reflectance decrease. The chlorosis effect in the NIR region is species-dependent, but for Norway spruce no consistent change is apparent (Hoque et al., 1988; Koch et al., 1990). This is because the leaf pigments play a minor role in the NIR region, where the leaf structure determines the amount of reflected light. Approximately 50% of the incoming radiation in the NIR is reflected by needles, which means that a needle loss, resulting in larger fractions of shadow (with no or very little reflectance), will have a strong effect. In the SWIR region, the water content of the target feature largely determines the amount of absorbed light. Koch et al. (1990) found that dark background reflected somewhat more light than did vegetation and observed no depressing influence by defoliation on the reflectance.

Ekstrand (1990) found a weak decrease in the reflectance with increasing defoliation. According to these results, the defoliation effect in the SWlR seems weak and somewhat ambiguous, perhaps ruled by the illumination geometry and the crown structure. The chlorosis results suggest a similar, weak effect in the SWIR. Koch et al. (1990) found no apparent increase in the reflection for yellow spruce needles, although brownish pine needles did exhibit such an increase. Similar results were presented by Guyot et al. (1989). Still, the pigment content is not likely to affect the SWlR reflectance. The explanation may be that needles turning brown probably have a depressed water content, resulting in the observed increase in reflectance. Following the results presented above, the only consistent spectral effect of forest decline when no chlorosis is evident is the reflectance drop in the NIR region. This is the situation usually found in the Nordic countries. Normally, a discoloration evolves below the tree top in the autumn which leads to defoliation within a few months. The persistent yellowing of sun exposed needles (chlorosis) feu_nd in central Europe has not been recorded in the study are~'. Forest Stand Parameters

Several forest stand parameters may have an influence on the reflectance that is equivalent to or larger than the influence of moderate defoliation, for example, species composition, density, understory, and age. Leckie (1987) found that variable spruce-fir or hardwood composition may result in confusion between insect defoliation classes. Earlier studies within this project (Ekstrand, 1990) suggested that the variation in the hardwood and pine components should be held very low to preserve a statistically strong relationship between Landsat TM and healthy to moderately defoliated spruce. Landsat TM data presented by Boresj6 (1989) from sites with varying compositions of Norway spruce, Scots pine and hardwood also indicated that the effects of such variations can be relatively large. Both hardwood and pine reflect more light than healthy and damaged spruce in all Landsat TM wavelength bands, which means that the confusing effects of these species are added to each other. In closed canopy conifer forest, structural conditions can be characterized on a continuum from simple in young, even-aged stands to complex in old-growth stands. Simple structured stands generally have a single canopy layer of similarly sized small trees with few canopy gaps, high tree density, and low basal area. Complex-structured stands commonly have numerous gaps, a variety of tree sizes, relatively low tree density, and high basal area. The more complex the stand, the greater the proportion of shadow present (Cohen and Spies, 1992). In old-growth forest basal area, volume, canopy closure, and density (trees per hectare) are usu-

Assessment of Forest Damage with Landsat TM 293

ally positively correlated to each other. Involving young stands means that density and crown closure become negatively correlated to the other two parameters. This is because young, well-managed conifer stands have extremely high density and crown closure but low basal area and volume. Studying old-growth conifer forest, Franklin (1986) found that as basal area increases from very low to high values, reflectance decreases in visible wavelengths while NIR reflectance first increases and then decreases, mainly due to shadowing as the canopy closes. Several authors have found that values in the range of moderate to high basal area are poorly correlated with stand reflectance (e.g., De Wulf et al., 1990; Franklin, 1986). Similar results have been presented for stand volume (m3/ha), calculated from basal area and average height (Ard6, 1992; De Wulf et al., 1990). Canopy closure can be predicted in the lower ranges, while results for middle and high ranges of canopy closure have been poor (Butera, 1986; Danson, 1987). These findings indicate that when the canopy closure has reached a point where the influence from the ground and from lower vegetation is diminished, a further increase in basal area, crown closure, or volume has only a limited effect on the stand reflectivity. The effect of changing understory is closely related to the density of the overstory. Earlier results have shown that understory affects the stand reflectance only when the overstory is sparse (Franklin, 1986; Sadowski and Malila, 1978). However, Stenback and Congalton (1990) found that the presence or absence of understory also gave a slight Landsat TM intensity response in forest with moderate canopy closure (30-70%). In stands with large hardwood and pine components, the diverse structure of the overstory can be expected to have an equivalence in the understory, which means that the understory would have a larger influence in those stands, provided that the overstory is not too dense. Age is an important factor influencing the reflectance from forest stands. The well-documented effect for spruce is a reflectance decrease with increasing age, somewhat more pronounced in the NIR and SWIR regions than in the visible (Kleman, 1986; Koch et al., 1990; Turner et al., 1988). The decrease has been shown to stop at approximately 40 years for Corsican pine (De Wulf et al., 1990) and at 60 years for Jack pine and black spruce (Horler and Ahern, 1986). Above that age, the reflectance is stable. Koch and Kritikos (1984) stated that since stands with advancing age behave spectrally similar to those with increasing damage, forest areas should be differentiated according to age when assessed for damage. It must be stressed that the main reflectance changes in an aging stand is caused by the changes in canopy structure associated with age. An important factor in this context is the sudden reduction in density due to the management practice of stand thinning. In

most parts of Sweden, the final thinning of spruce stands is carried out at the approximate age of 60 years, and the probable effect is a decrease in reflectance caused by a higher percentage of shadow within the canopy. METHODS Study Area A forest region in the county of Bohusl/in at the southwest coast of Sweden was selected for study (Fig. 1). The area is characterized by hilly terrain, although the local altitude differences are not very large, usually not more than 70 m. Between the hillocks are fiat, fine-grained sediments. The horizontal forest sites used for study are located on plateaus, valley bottoms, and hill tops in this terrain. Although the forest decline symptoms are moderate compared to the symptoms in some parts of central Europe, Bohusl/in suffers from higher levels of defoliation than other Swedish regions. The comparatively high defoliation levels in the area are generally attributed to long distance air pollutants together with local emissions of sulphur dioxides, nitrogen oxides, and hydrocarbons. The area is dominated by stands of Norway spruce (Picea abies), sometimes with large components of Scots pine (Pinus sylvestris) and mixed hardwood species like birch (Betula verrucosa and Betula pubescens), oak (Ouercus robur), and aspen (Populus tremula). The forest is divided into compartments of uniform age and species

Figure i. Location of the study area.

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composition. The stands are well managed, and the distribution of trees within each compartment is also relatively uniform. Most of the stands are 2-10 ha in area, although some are as small as 0.3 ha.

Acquisition and Processing of Satellite Data Landsat-5 TM imagery received at Esrange, Sweden, on 12 September 1985 and 28 August 1989 was acquired from the Swedish Space Corporation (SSC). The data from 1989 were geometrically corrected by SSC and resampled to 25 m. Dark object subtraction was done to all bands to reduce the effects of the atmosphere. The water reflectance in TM Bands 1-3 was set to 3.2%, 2.0%, and 0.4%, according to results presented by Bukata et al. (1983) for water with very small concentrations of suspended sediment and phytoplankton. These conditions were assumed to be similar to those in the lakes used here. The values are also close to clear ocean water reflectance presented by Gordon (1987). Reflectance was calculated using the equation defined by Markham and Barker (1985). The data from 1985 were not purchased geometrically corrected, and therefore the subimage covering the study area was resampied and registered to the 1989 image. Late summer Landsat TM scenes were chosen because earlier results within this project indicated that spruce defoliation increases during the summer and is more clearly distinguishable in late summer imagery (Ekstrand, 1990). The 1985 scene was included in the study for verification purposes. It was normalized against the 1989 data set using a relative calibration method. Although existing methods for absolute calibration constitute potential alternatives, uncertainties in the calibration process as well as in the estimation of the atmospheric effects may yield unacceptable errors (Horler and Ahern, 1986). The chosen relative calibration method used a regression function derived by plotting 1985 digital numbers from young forest areas (20-50 years) against 1989 digital numbers from the same areas. Young forest areas were used because no spectrally stable ground features of sufficient size were found within the satellite scene. Furthermore, Olsson (1991) found that regression functions computed from forest pixels performed better than regression functions based on dark and bright areas (water and gravel pits) or on all types of land cover classes. Using old forest in the function would have meant that any large scale spectral changes caused by forest damage were lost in the calibration (only old trees are affected by defoliation in the area). No management actions were taken in the young regression stands between 1985 and 1989, and therefore there was no spectral change other than the negligible effect of 4 years of aging. These stands included bright 20-year-old hardwood and darker 50-year-old spruce. The resulting regression equation was used to convert 1985 digital numbers into values comparable to 1989 values (Vogelmann

and Rock, 1989). This was repeated for all spectral bands. A drawback with the regression method is that light areas will be underestimated and dark areas will be overestimated, due to the regression fallacy effect (Olsson, 1991), which is caused by the fact that the traditional regression line of Y on X does not run through the midst of the regression points towards the ends of the point cluster. To reduce this effect, the regression function was calculated using Wald's method instead of the traditional regression lines. Wald's method fits the regression line by dividing the observations into two halves on basis of their X values. The line is then calculated from the mean values of each group (Curran and Hay, 1986; Wald, 1940).

Field Data and Reference Defoliation Assessment A total of 135 reference sites were selected and visited in the field during the month following the 29 August 1989 TM data acquisition. The parameters measured in field were age, basal area, timber volume, site quality, understory, slope, and aspect. Aerial photography was used to determine defoliation, species composition and density (stems per hectare). The size of the reference sites was 0.7-1.2 ha, which included 8-14 pixels when border pixels had been excluded. Larger homogeneous sites would have been desirable for statistical reasons, but could not be found within the air photo strips. The measured site characteristics from 1989 were assumed to be valid also for the 1985 TM data, except for defoliation, which was assessed in an earlier study using aerial photography from 1985 (Ekstrand, 1990). Site age was measured by visual inspection of tree morphology supported by annual ring counting on control trees. Mean basal area, mean height, and volume were determined for spruce, pine, and hardwood using relascope and clinometer. The species volume values were added up to from a plot total. Three to six plots in each site were examined, depending on the site homogeneity. Slope and aspect were determined using clinometer and compass. All 135 sites used had slope gradients less than 3 ° . The site density values presented here refers to the trees visible in the aerial photography. In comparisons with a small number of field measurements, these amounted to approximately 70% of all trees. The number of pine and hardwood trees within each site was also counted in the aerial photography. The size of the hardwood crowns sometimes varied significantly, for example, between birch and oak. This was considered in the aerial reference determination by counting large hardwood specimens as two trees. Determination of the defoliation on Norway spruce was accomplished by interpretation of color infrared (CIR) aerial photography on the scale of 1:6000, obtained 21 September 1989. The sole symptom of forest decline was defoliation (needle loss). No chlorosis was recorded in the aerial photography or during the field

Assessment of Forest Damage with Landsat TM 295

work. All clearly distinguishable trees in the 135 test areas, approximately 25,000 trees, were defoliationassessed. Generally, 40% of the trees in a stand were clearly distinguishable. The defoliation level of the remaining trees was studied during the field visits but was not found to deviate from the assessed trees. Each spruce tree was categorized into one of five 20% needle loss classes (0-20%, 21-40%, etc.) by photointerpretation. The interpretation was calibrated to the annual national field survey performed by the Faculty of Forestry, Swedish University of Agricultural Sciences. The mean spruce needle loss for each site was calculated by summing the number of trees in each category (e.g., 21-40%) and multiplying them with the class midpoint (e.g., 30%). The sums from the five categories were added up and divided by the total number of trees, resulting in a mean needle loss for the site, presented in percent. Theoretically, the minimum mean needle loss was 10% and the maximum 90%. The values for the 135 sites ranged from 13% to 37%. In a site with a mean needle loss of 30%, normally a good third of the trees were healthy (i.e., < 20% needle loss for the individual trees), around one third were slightly damaged (21-40% needle loss), and almost one third were moderately to severely damaged (41-100% needle loss).

Analyses of Spectral Contributions from Stand Parameters The mean reflectance was calculated for each reference site (8-14 pixels) and used in regression analyses of the relationship between satellite data and forest parameters. In the tests of a specific parameter such as defoliation or hardwood component, all other parameter values were held approximately constant by choice of reference sites included in the data set. For instance, in the defoliation set, consisting of 30 sites, the hardwood components varied between 0% and 3%, pine components between 0% and 5%, and the density between 170 st/ha and 270 st/ha. Thus, the parameter ranges were narrow, although still likely to cause some degree of the variance in the defoliation relationship. In the analysis of forest parameters, the defoliation was 20 _+4%. Examining one parameter, the others were held almost constant in the same way as in the defoliation set above. Some variations had to be allowed to provide a sufficiently large number of sites, although this may have caused somewhat lower correlation coefficients than if the parameters would have been absolutely fixed. In these analyses of the spectral contribution from stand attributes, field and air photo measured reference information on the attributes were used. Forest map attribute data were employed only in the final tests of the defoliation model. Stepwise multiple regression was employed to de-

termine the order of spectral significance among the forest parameters, with the forest parameters as independent variables. Simple linear regression was used to estimate the spectral response of each parameter, except for age in TM Band 4, which exhibited differing responses in different age intervals and was examined using both linear and nonlinear regression analysis. Curran and Hay (1986) stated that simple linear regressions may be inappropriate for calibration in many cases due to measurement errors in both the remotely sensed data and the ground variables. Since such errors existed here, although the magnitudes were not known, Wald's method to calculate an alternative regression line was applied (Curran and Hay, 1986; Wald, 1940). The approach was tested on the data sets; defoliation versus TM Band 4 and hardwood versus TM Band 4. The regression lines calculated with Wald's method gave a slight overestimation of both defoliation and hardwood component when inverted. Therefore, the traditional remote sensing technique based on a regression of Y on X was used. Generally, 20-30 reference sites were used in the regression analysis of each forest parameter. Through the derived regression lines, the spectral contribution of each parameter was determined. A model was developed that corrected for these contributions and then estimated the mean defoliation in each site. The model performance was evaluated by comparing predicted defoliation values with the observed field measured reference values. As in most remote sensing studies, the algorithms and the model presented here remain largely data-driven, because they are derived from data that have no direct biophysical link to the state of the forest canopy on the ground. However, the information contained in this article makes it possible to draw conclusions about the applicability of Landsat TM and the general form of the resulting model.

Integration of Forest Maps In order to apply the results of the stand attribute and defoliation analyses in operational conditions, General Forest Inventory (GFI) maps on a scale of hl0,000 were digitized. These maps were used to compare the model accuracy acquired when using field-measured stand attribute data in the model with the accuracy acquired with operational forest map data as attribute input. The area covered by digitized forest maps was 20,000 ha. The charts were supplied by the County Forestry Board, converted to raster data, and integrated as an information layer registered to the satellite data. Each forest type (e.g., 80% spruce, 10% pine, 10% hardwood, 200-250 m 3 / ha) was assigned a specific grey level in the raster data layers. In spite of the maps being reasonably current (3-8 years old), they were expected to bring two main error sources into the model:

296 Ekstrand

1. Species composition was categorized into tenths and not into percent. Supposing that the GFI field surveyor had estimated the species composition to the correct tenths (which according to comparison with the aerial photography was done in seven out of ten cases with no discernible bias in the estimation), the mean deviation from the correct percentage would be + 2.5%. 2. Since most of the study sites covered only part of a larger compartment, the stand parameter values of the map, estimated for the entire compartment, were not always correct for the specific site part. The test sites often covered the most "pure" part of a compartment, that is, the part where spruce forest was most dominating. Thus, except for the 10/10 spruce group, the forest map generally overestimated the pine and hardwood component for the test site. This effect was accounted for in the model by adjusting the correction made for pine/hardwood. However, the increased spreading of GFI pine / hardwood data compared to the field measured pine/hardwood component could not be accounted for. Therefore, the results for GFI data presented here is a "worst case." In operative inventories, the entire GFI compartment would be defoliation assessed, and this error source reduced. A minor error source worth noting is that the size of the hardwood tree crowns is not accounted for in the forest map data. Trees are included in the statistics if the diameter at breast height is sufficiently large, without consideration to species or crown size. A large oak or beech tree will have a spectral effect on the stand mean that is two or three times larger than that of a medium-sized birch tree. RESULTS AND DISCUSSION Defoliation TM Band 4 reflectance decreased distinctly with increasing defoliation (Fig. 2). Bands 3 and 5 also displayed a statistically significant decrease, although it was not as strong as in the data from 1985 (Ekstrand, 1990), partly depending on the somewhat narrower defoliation range in 1989. These results are in accordance with previous works in regions with defoliation as the dominant forest decline symptom (Koch et al., 1990; Rock et al., 1988). It should be noted that the range of digital numbers is low for all spectral bands (Fig. 2). In TM Band 4 one digital number equals a 6% defoliation change, which would be the minimum background noise level in pixelwise estimation. In standwise estimations, the background noise will be reduced since a mean value for each stand is calculated. Because of the decrease in the red and SWlR wave-

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other stand parameters were held approximately constant. length bands earlier suggested ratio indices such as TM Bands 5 / 4 and 4 / 3 (Rock et al., 1986; Vogelmann, 1990; Rosengren and Ekstrand, 1987) performed poorly (Table 1). A prerequisite for the ratios to yield improved results are that the trends of the numerator and the denominator are in clear variance with each other. According to the results presented here and in previous works referenced above, this is the case in forest areas exhibiting both chlorosis and defoliation, but not in regions where slight to moderate defoliation is the only symptom. Forest Stand Parameters The influence of age on spectral properties was studied in the range of 50-100 years. Although the influence of age is known to be larger for younger stands, these trees also have a lower sensitivity to stress and are less susceptible to the effects of forest decline than older

Table 1. Correlation Coefficients and Significance Levels for Landsat TM Ratio Indices versus Needle Loss Spectral Band or Index TM TM TM TM TM TM

4 5/4 4 /3 4 /2 4 ! (2 + 4 + 7) (4 - 3) / (4 + 3)

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0.001 > 0.05 > 0.05 0.05 0.05 > 0.05

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Assessment of Forest Damage with Landsat TM

trees. The age response in TM Band 4 proved to be large up to approximately 65 years (Fig. 7). From 70 years and up, the spectral effect was negligible in the NIR. Final thinning probably partly caused the reflectance drop around 60 years. The relationship with age was fairly strong also for TM Band 1, but weak for all the other spectral bands (Fig. 3). The age results are in accordance with previous work (e.g., De Wulf et al., 1990; Horler and Ahem, 1986), although the age in which the main spectral decrease occurred and the magnitude of that decrease are different between the species. It should be stressed that the best-fit nonlinear regression line for TM Band 4 (Fig. 7) is valid only for 50-100-year-old forest. The effect in forest younger than 50 years can be assumed to be significant, although not as strong as in the 50-70 year interval. In old forest, the hardwood component was the spectrally most significant single stand parameter besides defoliation. With components from 0% to approximately 25% both TM Bands 4 and 5 were correlated to hardwood component (Fig. 4). Since TM 4 was found to be the most powerful spectral band for defoliation assessment, the strong influence of hardwood in this band must be taken into consideration. Comparison of the defoliation and hardwood effects in Figure 7 reveals that a hardwood component of 20% in a compartment completely neutralizes the effect of a 20% needle loss. The effect of variations in the pine component was considerably lower than for hardwood but still significant for TM Band 4. With pine components between 0% and 25%, the correlation coefficient was 0.57 (Fig. 5). No other spectral bands had correlation coefficients that were statistically significant. In Figure 7 it is shown that the effect of pine variations of 25% is of the same magnitude in TM Band 4 as 10% hardwood variations

Figure 3. Correlation coefficients for Landsat TM bands vs.

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and 10% defoliation. The results for hardwood and pine meant that the method using digitized forest maps from the General Forest Inventory should be adequate, although resulting in a standard deviation increase in the defoliation estimation equivalent to 4-5% needle loss. The reason for this is that the division of species composition in tenths theoretically gives a mean deviation of +2.5% from the midpoint percentage in each tenth group, plus the GFI measurement errors. The density and timber volume of compartments older than 70 years were restricted to the medium and high ranges throughout the study area. This is likely to be the case in most parts of southern and central Sweden since the forest areas are intensively managed. The

Figure 5. Correlation coefficients for Landsat TM bands vs. pine component in stands dominated by spruce (25 sites, 0-25% pine). The lines show the 95% and 99% confidence limits.

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density in the reference sites ranged from 130 st / ha to 330 st/ha, and the timber volume from 120 m3/ha to 310 m 3 / ha. Density had a weak but statistically significant correlation with TM Band 4 (r = 0.61), while timber volume was not correlated to any of the spectral bands. Of these parameters, only timber volume is accounted for in the General Forest Inventory. Figure 6 displays the lack of relationship between Landsat TM and spruce timber volume in the range of 120-310 m 3/ha. The influence of understory has in previous work (Franklin, 1986; Sadowski and Malila, 1978) been shown to be limited to sparse overstory situations, with some possible small effects also in moderate canopy when the understory is dense (>50%). However, no old spruce sites with understories above 30% were found within the study area, obviously due to intensive management. Consequently, it was not meaningful to study the spectral effect of understory. The management practices are the same throughout southern and central Sweden, which implies that the understory spectral response is negligible for the problem studied. In very sparse sites the field vegetation layer influences the integrated spectral response. Only a few such areas were found within the study area, which made it difficult to study the disturbance effect. Sparse sites often have poor ground qualities and therefore comparably high defoliation levels due to a higher sensitivity; but they make up only a small part of the forest landscape of southern and central Sweden and can be disregarded in a relatively large-scale defoliation assessment. In other regions, though, it might be necessary to examine the field layer influence more carefully. The order of spectral significance among the stand parameters was determined by stepwise multiple regression analysis (Table 2). Hardwood component and

Table 2. Order of Spectral Significance among Compartment Parameters a Independent Variables

Partial Correlation

Age Defoliation Pine component Hardwood component Stems / ha

0.346 0.601 0.201 0.609 0.030

defoliation clearly had the strongest influence on TM Band 4, followed by age, pine, and stems / ha. The reason for the relatively low significance of age was that the linear equation employed in the multiple regression analysis failed to account for the differing response in different age classes. The simple regression scatter plots of TM Band 4 versus the stand parameters are found in Figure 7. A general description of the forest analyzed is that it includes old spruce forest with a density ranging from reasonably sparse to dense, containing minority fractions of pine and hardwood. Theoretically, the forest parameter ranges are narrow. There are, for instance, other species of interest. Still, the forest type studied is domi-

Figure 7. The spectral effects of defoliation and some forest parameters in TM Band 4. Reflectance (%) 14~r = .75 °°

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Assessment of Forest Damage with Landsat TM 299

nant in large parts of northern and central Europe, including Sweden, and it is the forest type on which most European field and airborne surveys have been focused.

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Defoliation Assessment Model Using the results presented above, a model that accounted for compartment specific variations in the stand parameters by modifying the TM Band 4 stand reflectance was formulated. Age was accounted for by excluding stands younger than 70 years from defoliation estimation. This decision was based on the facts that younger spruce forest was not suffering from forest decline symptoms, and that the age spectral response was stable from 70 years and up. If an analysis of the age group 50-70 years will be of future interest, a correction for age can be calculated from the slope of the TM Band 4 regression line in Figure 3. Variations in timber volume gave no spectral response in the range examined here (120-310 m3/ha). However, to eliminate situations where understory and field layer might affect the result, compartments with volumes lower than 120 m3/ha were excluded. Also stands with volumes above 310 m 3/ha were excluded since the spectral response in that range was not known. The extremes ruled out composed 6% of the old spruce compartments in the area. The remaining two factors, hardwood and pine component, had to be corrected for in a more detailed manner. Based on the regression lines for TM Band 4 presented in Figure 7, a correction model for forest on level ground was computed. Rc(0.83) = R(0.83) - 0.0318P- 0.0756H,

(1)

where R~(0.83) is the corrected compartment reflectance (%) of TM Band 4, R(0.83) is the original compartment reflectance, P the pine component in percent, and H the hardwood component in percent. The constants express the spectral effects of 1% pine and 1% hardwood, respectively, derived from the regression lines in Figure 7. The defoliation (needle loss in percent) was then estimated by the inverted regression function of TM Band 4 on needle loss: D = [Rc(0.83) - 11.887]/( - 0.0920).

(2)

Equations (1) and (2) were verified by regression analysis of predicted versus observed defoliation. Forty unused reference sites were employed in the main verification set. The spruce fraction was 75-100%, and the pine and hardwood fractions varied between 0% and 25%. When "true" field and air-photo-measured information on the stand parameters were used in the model, but with species composition percentages converted to tenths, the results were very good (Fig. 8). Dividing the defoliation into two classes, below and above 25% needle loss, resulted in a classification accuracy of 85 %.

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Figure 8. Predicted vs. observed defoliation when field and air photo determined stand characteristics were used in the model.

The final results were acquired when information on the stand parameters was taken from the digitized forest maps and not from field / air photo measurements. The problem associated with the study sites covering only a part of larger compartments was as described earlier partly accounted for in the model. However, some of the sites were still assigned an incorrect pine / hardwood component. Therefore, Figure 8 with airphoto-interpreted pine/hardwood information used as attribute input to the model is a "best case," and Figure 9 with forest map pine/hardwood information used as attribute input a "worst case." The operative accuracy will lie somewhere in between. When employing forest map data instead of field measured "truth" in the correction procedure, the model accuracy decreased from 85% to 80%. Five sites in Figure 9 were clearly underFigure 9. Predicted vs. observed defoliation when forest map stand characteristics were used in the model. 40 z o F-
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estimated by the model. This was caused by a smaller hardwood component in the forest map data than in reality due to the sites covering only part of the compartment. Such a problem would not appear in large scale surveys. The entire compartment would then be assessed. To reach the lower level of 80% in operative inventories, the quality of the forest maps would have had to drop to a level where the field surveyor had estimated the species composition to the correct tenth in only six out of ten cases. Homogeneity within the test sites was necessary to be able to study the spectral effects of forest parameters. Nature, however, sometimes behaves differently in spite of intensive management. In future, it may prove necessary to use standwise assessments instead of pixelwise, because of heterogeneity within the stands. Although the correction for stand parameters was basically successful, increasing pine and hardwood fractions will eventually diminish the spectral contribution of spruce, and accordingly the effect of spruce defoliation. In order to further analyze this effect, the defoliation estimation accuracy was determined in four verification sets with different mixtures of spruce and pine / hardwood; one set for pure spruce stands (14 sites), one for 9/10 of spruce (31 sites), one for 8/10 (25 sites), and one for 7/10 (18 sites). These sets were made up by the 40 unused reference sites plus sites from the age and volume analysis (>70 years, moderate density). The "best and worst case" classification accuracies are listed in Table 3. The accuracies of 10 / 10 and 9 / 10 spruce sites were good. The result for 8 / 10 of spruce indicated that the accuracy might be acceptable if the forest maps are of very high quality. For 7 / 10 spruce sites the accuracy was clearly unacceptable. Examination of residuals showed a slightly weakened model performance with increasing pine/hardwood component, although it was not clear enough to explain the low accuracies in the 8 / 10 and 7 / 10 spruce groups. This indicates that an additional cause is the reduced spectral contribution from spruce and spruce defoliation in mixed forest. It should be stressed that the two causes are difficult to separate, since it is impossible to know which one of them is causing errors

Table 3. Best and Worst Case Accuracies with Two Defoliation Classes in Groups with Decreasing Spruce Components Stand Type

Best Case Accuracy

Worst Case Accuracy

10 / 10 of spruce 9 / 10 of spruce 8 / 10 of spruce 7 / 10 of spruce

93% 79% 70% 61%

88% 75% 63% 54%

in sites with large pine/hardwood components (and consequently less spruce trees). The model was also verified on Landsat TM data from 1985. After correction for stand parameter disturbance employing the forest maps, the defoliation levels were estimated using Eqs. (1) and (2). One strip of aerial CIR photography from 1985 was available for use in the reference defoliation assessment. Twenty-two reference sites could be located within this strip, all with spruce components of 80-100%. The defoliation classification accuracy for the 1985 verification set was 73%. The somewhat lower accuracy compared to the data set from 1989 was probably caused by the interscene calibration, which is never completely correct, and possibly by the reference defoliation assessment, performed using aerial CIR photography on the scale of 1:10,000. With this scale the defoliation estimation is slightly less accurate than with 1:6000, which was used for the 1989 reference assessment. The reference assessment of 1985 was used only to compare predicted defoliation with observed. Including a small amount of field or aerial defoliation reference data in new inventories, used to modify the constants of the model if necessary [Eqs. (1) and (2)], will probably make it possible to maintain a defoliation accuracy of approximately 80%. CONCLUSIONS Analyses of data from 1985 and 1989 suggest that the decrease of Landsat TM Band 4 reflectance is the only consistent spectral effect of moderate defoliation on Norway spruce. The best estimate of defoliation was acquired with an algorithm based solely on TM Band 4. Earlier suggested ratio algorithms, found to be applicable in regions suffering from both chlorosis (yellowing) and defoliation, seem inappropriate in areas where defoliation is the sole symptoms of forest decline. Several compartment parameters were found to adversely affect the defoliation assessment, and had to be attended. For age it was sufficient to exclude compartments younger than 70 years using the forest maps, and for density and understory to exclude very sparse and very dense compartments. The two factors with the strongest spectral response were hardwood component and pine component. The correction for these factors was accomplished with a model that used forest map information to modify the stand reflectance. The classification accuracy for two defoliation classes derived with the model was good for sites with a spruce component of 85-100%, barely acceptable for sites with 75-84% spruce, and unacceptable for compartments with 65-74 % spruce. Dividing the defoliation into two classes, below and above 25% mean needle loss, gave a "worst case" classification accuracy of 80% in sites with 75-100% spruce. The successful separation of slight and moderate

Assessment of Forest Damage with Landsat TM 301

defoliation presented here was carried out in forest areas on level ground. To extend the feasibility, topographic effects have to be considered in the model. This problem will be studied in follow-up projects. Another concern is the amount of work needed to digitize forest maps. A survey on a national scale would require extensive resources for that purpose and may be impractical at the time being. However, several forest companies are now converting their management maps to digital databases, and therefore inventories on a provincial scale seem feasible. Funding for this research was provided by the Swedish Environmental Research Institute and the Swedish Board for Space Activities. Many thanks to Professor Friedrich Quiel for valuable comments on this article and constructive suggestions during the course of the project. Special appreciation goes to Field Assistant Jenny Samuelson.

REFERENCES Ard6, J. (1992), Volume quantification of coniferous forest compartments using spectral radiance recorded by Landsat Thematic Mapper, Int. J. Remote Sens. 13:1779-1786. Boresj6, L. (1989), Landsat TM and SPOT data for mediumscale mapping of Swedish vegetation types, Thesis, National Swedish Environmental Protection Board, Report 3571, Solna, Sweden. Bukata, R. P., Bruton, J. E., and Jerome, J. H. (1983), Use of chromaticity in remote measurements of water quality, Remote Sens. Environ. 13:161-177. Butera, K. M. (1986), A correlation and regression analysis of percent canopy closure versus TMS spectral response for selected forest sites in the San Juan national forest, Colorado, IEEE Trans. Geosci. Remote Sens. 24:122-128. Ciolkosz, D. L., and Zawila-Niedzwiecki, T. (1990), Remotely sensed data and limitations of forest productivity in Poland, Nature Resources 26:41-44. Cohen, W. B., and Spies, T. A. (1992), Estimating structural attributes of Douglas fir/western hemlock forest stands from Landsat and SPOT Imagery, Remote Sens. Environ. 41:1-17. Curran, P. J., and Hay, A. M. (1986), The importance of measurement error for certain procedures in remote sensing at optical wavelengths, Photogramm. Eng. Remote Sens. 52:229-241. Danson, F. M. (1987), Preliminary evaluation of the relationships between SPOT-1 HRV data and forest stand parameters, Int. J. Remote Sens. 8:1571-1575 (letter). De Wulf, R. R., Goossens, R. E., De Roover, B. P., and Borry, F. C. (1990), Extraction of forest stand parameters from panchromatic and multispectral SPOT-1 data, Int. J. Remote Sens. 11:1571-1588. Ekstrand, S. (1990), Detection of moderate damage on Norway spruce using Landsat TM and digital stand data, IEEE Trans. Geosci. Remote Sens. 28:685-692. Franklin, J. (1986), Thematic Mapper analysis of coniferous forest structure and composition, Int. J. Remote Sens. 7:12871301. Gordon, H. R. (1987), Calibration requirements and methodol-

ogy for remote sensors viewing the ocean in the visible, Remote Sens. Environ. 22:103-126. Guyot, G. (1983), Approach des facteurs de variation de la response spectrale du Pin maritime en Foret landaise, in H CoUoque International Signatures Spectrales d'Objets en Te'l~ddtection, Les CoUoques d I'INRA, Bordeaux, France, 12-16 September 1983, Les colloques de I'INRA No. 23, INRA, Avignon, pp. 209-217. Guyot, G., Guyon, D., and Riom, J. (1989), Factors affecting the spectral response of forest canopies: a review, Geocarto Int. 1989 (3):3-18. Hartmann, G., and Uebel, R. (1986), CIR-Luftbildinterpretationsschlussel zur schadenansprache an fichte im Harz, Der Forst- Holzwirt 16:438-441. Hoque, E., Hutzler, P. J. S., and Seidlitz H. K. (1988), Relationship between discoloration and histological changes in leaves of trees affected by forest decline, Remote Sens. Environ. 26:171-184. Horler, D. N. H., and Ahern, F. J. (1986), Forestry Information of the Thematic Mapper, Int. J. Remote Sens. 7:405-428. Kleman, J. (1986), The spectral reflectance of stands of Norway spruce and Scotch pine, measured from a helicopter, Remote Sens. Environ. 20:253-265. Koch, B., and Kritikos, G. (1984), Integrative investigation on forest damage detection based on airborne multispectral scanner data, in Proceedings of the Symposium on Integrated Approaches in Remote Sensing, Guildford, United Kingdom, ESA SP-214, Paris, pp. 109-113. Koch, B., Ammer, U., Schneider, T., and Wittmeier, H. (1990), Spectroradiometer measurements in the laboratory and in the field to analyse the influence of different damage symptoms on the reflection spectra of forest trees, Int. ]. Remote Sens. 11:1145-1163. Leckie, D. G. (1987), Factors affecting defoliation assessment using airborne multispectral scanner data, Photogramm. Eng. Remote Sens. 53:1665-1674. Markham, B. L., and Barker, J. L. (1985), Spectral characterization of the Landsat Thematic Mapper sensors, Int. ]. Remote Sens. 6:697-716. Olsson, H. (1991), Standwise change detection-experiences from studies in northern Sweden, in Proceedings of the Conference on Remote Sensing for Forest Inventory, IUFRO, Copenhagen. Riom, J. (1987), Monitoring forest decline in France by remote sensing, in Proceedings of the Seminar on Remote Sensing of Forest Decline Attributed to Air Pollution, Laxenburg, Austria, IIASA, Vol. 14, pp. 1-9. Rock, B. N., Vogelmann, J. E., Williams, D. L., Vogelmann, A. F., and Hoshizaki, T. (1986), Remote detection of forest damage, BioScience 36:439-445. Rock, B. N., Hoshizaki, H., and Miller, J. R. (1988), Comparison of in situ and airborne spectral measurements of the blue shift associated with forest decline, Remote Sens. Environ. 24:109-127. Rosengren, M., and Ekstrand, S. (1987), A method aiming at monitoring of large-area forest decline using satellite imagery, in Proc. Seminar on Remote Sensing of Forest Decline Attributed to Air Pollution, Laxenburg, Austria, IIASA, Vol. 2, pp. 1-20. Sadowski, F. G., and Malila, W. A. (1978), Investigation of techniques for inventorying forested regions: reflectance

302

Ekstrand

modeling and empirical multispectral analysis of forest canopy components, in NAS-CR-ERIM-122700-35-FL Environmental Research Institute of Michigan, Ann Arbor, Vol. 1. Stenback, J. M., and Congalton, R. G. (1990), Using Thematic Mapper imagery to examine forest understory, Photogramm. Eng. Remote Sens, 56:1285-1290. Teillet, P. M., Leckie, D. G., Ostaff, D., Fedosejevs, G., and Ahern, F. J. (1985), Spectral measurements of tree defoliation, in Proceedings of the 3rd Int. Colloq. Spectral Signatures of Objects in Remote Sensing, Les Arcs, France, European Space Agency, Paris, pp. 511-516. Turner, B. J., Moore, D. M., and Skidmore, A. K. (1988), Forest Management applications of SPOT data in Australia, Proceedings of SPOT 1 Symposium "Image Utilization .... ~Toulouse CNES Cepadures ed., pp. 953-959. Vogelmann, J. E. (1990). Comparison between two vegetation indices for measuring different types of forest damage in

the north-eastern United States, Int. J. Remote Sens. 11: 2281-2297. Vogelmann, J. E., and Rock, B. N. (1989), Use of Thematic Mapper data for the detection of forest damage caused by the pear thrips, Remote Sens. Environ. 30:217-225. Wald, A. (1940), The fitting of straight lines if both variables are subject to error, Ann. Math. Stat. 11:162-186. Wastenson, L., Aim, G., Kleman, J., and Wastenson, B. (1987), Swedish experiences of forest damage inventory by remote sensing, in Proc. Seminar on Remote Sensing of Forest Decline Attributed to Air Pollution, Laxenburg, Austria, IIASA, Vol. 16, pp. 1-31. Westman, W. E., and Price, C. V. (1988), Detecting air pollution stress in southern California vegetation using Landsat Thematic Mapper band data, Photogramm. Eng. Remote Sens. 54:1305-1311.