Radiation-Vegetation Relationships in a Eucalyptus Forest€¦ · Lallt Kumar and Andrew K. Skldmore Abstract ... it will not work ade- quately in hilly regions where shading by topographic

Radiation-Vegetation Relationships in a Eucalyptus Forest

Lallt Kumar and Andrew K. Skldmore

Abstract Radiation-vegetation relationships for 12 species of Eucalyptus from the south coast of New South Wales showed that the mean radiation values differed significantly between species. Confidence intervals around the mean radiation values, based on pooled standard deviation, were used as an index of species generality. There were differences in the size of the confidence intervals between species. While E. bosistoana and E. maidenii had large confidence intervals over all seasons, E. sieberi, E. muellerana, and E. globoidea had very small confidence intervals. The species also exhibit a shift in the relative positions of their confidence intervals according to season. Clearly, radiation data are an important variable for eucalypt species delineation, and may be included as an explanatory variable when modeling the distribution of eucalypts.

Solar radiation was calculated for the different seasons of the year using a model developed within a geographic information system (CIS); the input to the model is simply a digital elevation model ( D E ~ and the latitude of the site.

Introduction A number of response models have been developed to investi- gate the relationships between different environmental factors and distribution of eucalypt species (e.g., McColl, 1969; Austin et al., 1984; Austin et al., 1990; Moore et al., 1991). These models have included environmental variables such as nutrient availability, rainfall, temperature (Moore et al., 1991), topographic position (Austin et al., 1983; Austin et al., 1994), elevation, aspect, exposure to wind (Mosley, 1989), slope position ('hvery et al., 1991), soil structure (Florence, 1981), and soil nutrients (Turner et al., 1978).

Some of these models have used a solar radiation index for vegetation mapping (Kirkpatrick and Nunez, 1980; Austin et al., 1983; Moore et al., 1991; Ryan et al., 1995), with Kirkpatrick and Nunez (1980) reporting a strong correlation between solar radiation and the distribution of several species of eucalypts along a single transect in the Risdon Hills in Tasmania. These models have calculated solar radiation over individual field plots through field-measured parameters using the method suggested by Fleming (1971) or have used radiation measuring devices, such as pyranometers (Kirkpatrick and Nunez, 1980). While solar radiation data collected in the field are generally the most reliable, it is very difficult to extrapolate these data to other sites or over a large area, especially in mountainous terrain where solar radiation is strongly influenced by terrain.

Such data, based on point samples measured in the field, are not suited to spatial modeling in a GIS.

Solar radiation indices calculated in a GIS and based on slope aspect and slope gradient are crude estimates as they do not take into account shading by adjacent terrain. While such a method may be acceptable in flat areas, it will not work ade- quately in hilly regions where shading by topographic features can account for very large differences in the radiation received at a site (Kumar et al., 1997). Simulations in a mountainous area by Hetrick et al. (1993) showed that topographic shading was more important than surface orientation. For the site used in this study, it was found that, when shading by topographic features was included, approximately 30 percent of grid cells had their total radiation reduced by 10 percent. In this project, solar radiation was calculated by a GIS program using input information including a DEM, Julian date, and latitude (Kumar et al., 1997). Solar radiation can be calculated and integrated for short periods (e.g., a day) or, alternatively, integrated over a season or year.

Previous research (e.g., Austin et al., 1984) showed the distribution of vegetation responding directly to environmental factors, including temperature, moisture regime, and nutrient availability; and, because temperature and moisture regime may be linked to solar radiation (Ahrens, 1982), it is hypothe- sized that the distribution of vegetation should be related to solar radiation. The aim of this study is to confirm whether the distribution of eucalypt species are related to differences in solar radiation incident at a grid cell.

Study Site The study site, comprising parts of Nullica State Forest, Gnupa State Forest, and Yowaka National Park, is situated near Eden, New South Wales, at a latitude of 36.5' south. The site varies fiom rolling hills to fairly rugged terrain, with elevation ranging from 80 to 880 meters. Figure 1 shows the location of the site, while Figure 2 gives a summary of the slope, aspect, and elevation distributions. The total area of the study site is 16,406 hectares, stored as 182,288 30- by 30-m grid cells in a GIs. The study site is sufficiently small to assume mesoclimatic variables are constant (Ahrens, 19821.

The climate of the study area is typical of the south coast of New South Wales, with mean annual temperature ranging from 12 to 14 degrees Celsius (Keith and Sanders, 1990) and mean annual rainfall of approximately 1000 mm (Forestry Commission of New South Wales, 1982). The geology of the area is dominated by the Upper Devonian Boyd Volcanic complex (Delaney, 1996) and the soils are mostly acidic, highly

L. Kumar was with the School of Geography, University of New South Wales, Sydney, NSW 2052, Australia. He is pres- ently with the Tropical Nature Conservation Group, Wagen- ingen Agricultural University, Bornsesteeg 69, 6708 PD Wageningen, The Netherlands ([email protected]).

Photogrammetric Engineering & Remote Sensing Vol. 66, No. 2, February 2000, pp. 193-204.

A.K. Skidmore is with Agriculture, Conservation and Environ- ment, International Institute for Aerospace Survey and Earth Sciences (ITC), PO Box 6,7500 AA Enschede, The Netherlands.

PHOTOQRAMMETRIC ENGINEERING & REMOTE SENSING

0099-1112/00/6602-193$3.00/0 8 2000 American Society for Photogrammetry

and Remote Sensing

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Figure 1. Location of study site.

weathered, and poor to medium in nutrient content (Bridges and Dobbyns, 1991).

The study area is coveredby mostly dry schlerophyll forest (Baur, 1965), where the overstorey tree canopy is totally dominated by Eucalyptus species. In gullies, some wet schlerophyll forest appears (Baur, 1965). The parent material is Devonian granite, with soil formations being mostly podzolic, though gley soils form in areas of colluvial deposition.

One major paved road passes through the area, and a number of fire access trails exist. The area is subject to periodic wildfire, though the forest generally recovers rapidly (Gill et al., 1981). Human activities have been limited to low intensity fires purposely lit to reduce fire fuel loads. In addition, some small areas within the study area have been harvested to produce sawlogs and woodchips for paper pulp manufacture. These areas have had a larger number of higher quality roads con- structed for access.

Data The data were collected using 102 plots with dimensions of 50 meters by 20 meters. For each field plot, the number of trees of each species were recorded. Other information such as field estimates of slope, aspect, and topographic position were cross-checked against the same information derived from a DEM; no serious errors were identified.

Plots were selected randomly, stratified by geology and forest type.

194 February 2000

Figure 2. Summary of slope, aspect, and elevation for study site.

Solar Radiation Data Solar radiation for the study area was calculated using the method proposed by Kumar et al. (1997). In brief, solar radiation received at a site is dependent on the altitude and elevation of the sun, surface gradient (slope) and orientation (aspect), as well as position relative to neighboring surfaces. Variables such as solar azimuth and solar altitude angles change continuously throughout the day and so they have to be calculated every time the intensity of solar radiation is computed. Another important factor which needs to be calculated instantaneously is shading by topographic features. In contrast, solar declination may be calculated daily, as it varies more gradually.

PHOTOGRAMMETRIC ENGINEERING & REMOTE SENSING

Aspect and slope may be easily calculated from a DEM (Skidmore, 1989; Skidmore, 1990). The additional inputs required to derive solar radiation are the latitude of the site and the Julian date (note that if calculations are required for more than one day, the start and end day are needed). For integrating the total radiation over a period (i.e., days, weeks, or months), the repeat period between the instantaneous calculation of solar flux must be specified by the user. While it would be ideal to have a very short time interval to obtain accurate results, this is not always feasible because of constraints such as computa- tional expense and the availability of a fast computer. The time interval chosen can be larger for flat terrain but has to be smaller for mountainous regions as shadowing effects will be accentuated in such environments (Kumar et al., 1997). The radiation flux is calculated at the mid-point of each time interval to reduce shadowing effects.

The Kumar et al. (1997) algorithm is written in both Arc- Info and Genamap scripts, and is available at ftp site fatboy. geog.unsw.edu.au (under the solarradn directory). For this study, a time interval of 30 minutes was used because a comparison of radiation values produced using 30-minute and one- minute time intervals showed that, on average, the 30-minute interval values differed by only about one percent when compared to the one-minute interval values. Radiation flux was calculated for each season, namely summer (December to Feb- ruary), autumn (March to May), winter (June to August), and spring (September to November).

Method of Analysis Of the 18 species of eucalypts present in the surveyed plots, six species were discarded because they were too rare for statistically valid results. For the other species, interrelationships between species distribution and solar radiation were studied by looking at

all trees present, mature trees dominating the forest canopy, and dominant species for each plot.

"Mature trees" were defined as being greater than 20 meters in height. In an old growth forest, such trees give an indication of species distribution as they represent the dominant canopy species. The "dominant species" was the species within each plot with the largest proportion of trees. Table 1 lists the species used as well as the locations in which they are generally-found.

Due to the forest having a natural mix of species in each plot, the data were pre-processed before analyzing the relationship between species distribution and solar radiation. In order to generate an index of solar radiation adjusted for the species composition of the plot, the radiation values for each plot were

TABLE 1. LIST OF EUCALYPTUS SPECIES USED AND THEIR GENERAL LOCATION

Eucalyptus Species Location

E. sieberi Ridges and hilly country E. muellemna Moist sheltered valleys E. viminalis Moist, well drained valleys in hilly

country E. cypellocarpa Mountainous or hilly country E. agglomerata Gentle moderate slopes and escarpments E. obliqua Hills and mountains E. elata Fertile valleys E. smithii Low slopes and river flats E. globoidea Undulating steep slopes E. consideniana Poorly drained and low nutrient soils E. bosistoana Tall open forest and river flats E. maidenii Moist, sheltered valleys and high rain-

fall ridges

(Source: Boland et al., 1992)

normalized according to the number of trees of each species present. Thus, for a particular species (such as Eucalyptus sieb- en], a "weighted mean" of the radiation values was calculated by multiplying the radiation value of each plot by the number of trees of Eucalyptus sieberi in the plot, adding up these values, and then dividing by the total number of trees of E. sieberi in all the plots. The "weighted mean" radiation value, therefore, represents the overall radiation zone preferred by each species. This index emphasized the plots that a particular species preferred, because the frequency of occurrence gives an indication of the environment preferred by those species. If the plot data are not normalized by the number of trees, then a plot with say 100 trees of E. sieberi has the same weight as a plot which has only one tree of E. seiberi, and both will contribute equally to the analysis. The decision to normalize is based on the observation that the forest structure is composed of "old growth," which is dominated by an overstorey of large and medium sized trees.

A research hypothesis, that the "mean weighted" radiation values differed between species, was tested using the F-test. Stated formally, the null hypothesis is that the mean solar radiation for different species are equal, that is,

while the alternative hypothesis is that there is a difference in mean solar radiation between species 1 and species 2, that is,

for a' < 0.05. Species groupings were investigated using multivariate

techniques such as cluster analysis. Due to the large number of cases, the K-Means cluster analysis algorithm, based on the nearest centroid sorting (Anderberg, 1973), was used for determining cluster membership.

Results and Discussion Radiation Modeling Figure 3 shows the variation in short wave solar radiation across the study area for the different seasons, ranging from 3,600 to 11,200 h4J/m2/year. Exposed sites received almost three times as much solar radiation as shadowed sites.

The seasonal difference in the solar radiation is large, and, as expected, the summer season receives more radiation than the winter season (Table 2 details the maximum, the minimum, and the standard deviation values for each of the seasons). The mean radiation is highest in summer, followed by spring, autumn, and winter, respectively, with the mean winter value being 60 percent of the summer value. Many gully sites do not receive any direct radiation during winter because they are con- tinually shaded by adjacent ridges.

The variance in solar radiation is least during summer and largest in winter, especially for the south, southeast, and southwest aspects. These aspects have the largest variation on their midslopes, caused by shading when the sun is lower in the winter sky.

Figure 4 shows the distribution of radiation by aspect and slope for the whole year. North facing slopes receive far more radiation than the south facing slopes, with other aspects ranging between these two extremes. While the radiation values for north, northeast, and northwest aspects stay almost constant with slope, those for south, southeast, and southwest fall off fairly sharply as the slope increases. At low slopes, there is little difference in the radiation values for the different aspects, with the differences between aspects being least in the summer months.

PHOTOGRAMMETRIC ENGINEERING 81 REMOTE SENSING February 2000 195

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r Seasnal Average Dally Solar Rad~at~on D ~ n r ~ b u t ~ o n

Figure 3. Solar radiation distribution for the different seasons.

TABLE 2. SUMMARY OF SOLAR RADIATION DATA FOR DIFFERENT SEASONS

Summer Autumn Winter Spring Total

Min. 1761 477 0 1388 3647 Max. 3355 2631 2102 3198 11253 Mean 2762 2044 1634 2518 8958 St. Dev. 150 298 356 199 928

(Note: Units for radiation data are MJ/m2/season)

mature trees greater than 20 metres in height were considered in Figure 5b. In Figure 5c, the dominant species for each plot were used. Figure 5d shows the relationship between plots with species constituting more than 50 percent of trees for a plot and the "weighted mean" solar radiation. Note that all species do not appear in all the graphs. In some cases, no trees of a particular species met the conditions for inclusion; for example, in Figure 5d E. maidenii is missing because it did not constitute more than 50 percent of the trees in any plot.

Figure 5 shows that different species prefer certain radiation zones. Of the 1 2 species in this study, E. consideniana occurs at sites with the lowest radiation values. E. smithii also appears at the lower end of radiation values in all the figures. Similarly, E. obliqua consistently occurs on low radiation sites. E. agglomerata, E. bosistoana, and E. viminalis are consistently placed at the high end of radiation values for Figures 5a to 5d; while E. cypellocarpa and E. sieberi occur at middle to high radiation values.

Seasonal variation in solar radiation also appears to influence the occurrence of eucalypt species. Figure 6 details the radiation values for each season separately and Figure 7 emphasises the positions of different species in relation to their summer and winter radiation values. While the results are similar to Figure 5, there are some other features worth noting, specifically:

E. consideniana is found at sites having relatively low summer and winter radiation values. The pattern is consistent throughout all the graphs, i.e., whether one considers all trees present in the plots, mature trees only, or looks at dominant species only. Of all the species studied, E, consideniana is most separate from all other species (Figure 7). E. smithii also has characteristics

Species DlsMbuNon similar to E. consideniana. Exploratory data analysis reveals that there are large differ- E. bosistoana occurs in plots having high radiation in all sea-

sons, but occurs on sites with particularly high radiation in ences in the radiation regimes between some of the species. winter. E. virninalis also shows similar characteristics (Figure 7). Figure 5 shows the relationship between the "weighted mean" When the dominant species of each plot are considered (Figure solar radiation and the different species. For Figure 5a, all trees 6), E. agglornerata is at the high end of the radiation regime present in the plots were used, irrespective of height, but only for all seasonal combinations.

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196 February 2000 PHOTOGRAMMETRIC ENGINEERING & REMOTE SENSING

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PHOTOGRAMMETRIC ENGINEERING & REMOTE SENSING February 2000

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Figure 7. Species position in relation to their summer and winter radiation values. 11. E. agglomerata, 2. E. bosistoana, 3. E. consideniana, 4. E. cypellocarpa, 5. E. elata, 6. E. globoidea, 7. E. maidenii, 8. E. muellerana, 9. E. obliqua, 10. E. sieberi, 11. E. smithii, 12. E. viminalis]

The results of Figure 7 are generally in agreement with results of Figure 7 confirm this. Both E. sieberi and E. cypello- those reported by previous researchers and summarized in carpa are found at sites with high solar radiationvalues in both Table 1. From Table 1 we see that E. sieberi and E. cypellocarpa summer and winter seasons. E. agglomerata occurs on moder- occur on mountainous or hilly country and, therefore, would ate slopes and escarpments, and so would be expected to have be expected to fall in the high solar radiation regime. The a moderate radiation regime. Figure 7 shows that this is indeed


the case. The low winter radiation for E. consideniana suggests that it prefers locations with a high degree of shading during the whole year.

Some of the species whose radiation regimes differ sub- stantially from that reported in Table 1 include E. muellemna and E. viminalis. For E. muellemna, the higher than expected summer insolation indicates that this species probably prefers a drier location than expected. Similarly, E. viminalis has a higher insolation regime than one would expect fiom Table 1. More research is needed to confirm the preferred sites for these species.

It was confirmed statistically that the eucalypt species occur on sites with radiation values that characterize each species. There is a statistically significant difference in radiation values between species, as tested usingthe one-way analysis of variance test (for a' < 0.05). Figures 8 and 9 show the 95 percent confidence intervals around the mean radiation value for each species using all the trees found in the plots (Figure 8) and only mature trees (Figure 9); and Table 3 shows their F values obtained from the one-way analysis of variance. Both figures show that there are fairly large differences in the confidence intervals between the species as well as in different seasons, and Table 3 c o n h s that these differences are statistically significant. E. sieberi and E. globoidea are seen to have consis-

tently small confidence intervals over all the seasons while E. maidenii and E. smithii have the largest confidence intervals.

Which species are significantly different was tested using post-hoc multiple comparison tests, including Student-New- man-Keuls Test and Tukey's Honestly Significant Difference Test. Tables 4 and 5 give the results obtained using Student- Newman-Keuls Test, with all trees used for Table 4 and only mature trees used for Table 5. The results show that, for many of the species, the differences in the mean radiation are statistically significant. For example, E. consideniana has insolation values which are significantly different from all species (considering all trees) for all the seasons, except in spring and summer where E. smithii is an exception. Similarly, E. obliqua is significantly different from all the species for all the seasons when all trees are considered, and significantly different from all species except E. maidenii when mature trees are considered. Some other species which return significance with many of the species are E. agglomerata, E. bosistoana, E. cypellocarpa, E. globoidea, andE. muellemna. When only mature trees were considered, the number of significant pairs were fewer, with E. muellerana and E. obliqua returning significance with most of the species in all four seasons. Similar results, as reported in Tables 4 and 5, were obtained using Duncan's Multi- ple Range test, while Tukey's Honestly Significant Difference

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TABLE 3. F VALUES OBTAINED FROM A ONE-WAY ANALYSIS OF VARIANCE

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test returned a slightly fewer number of significant pairs because of its more conservative nature.

Another point of interest is the way some of the confidence intervals shift between seasons, indicating that seasonal differences in radiation may assist in characterizing species. For example, E. sieberi has a confidence interval placed at the high end of radiation in summer and is at the upper end of the interval for E. viminalis but moves lower in autumn and winter seasons and, when compared to E. viminalis, is at the lower end of its confidence interval (Figure 8). Similarly, if one compares E. agglomerata and E. cypellocarpa, the 95 percent confidence intervals of mean insolation for E. agglomerata is placed lower than that for E. cypellocarpa for the summer season, but during

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Figure 9. Individual 95 percent confidence intervals for means based on pooled standard deviation; using weighted radiation data (considering mature trees only).

autumn and winter it is higher than E. cypellocarpa. E. cypellocarpa and E, elata also show a similar tendency. Therefore, while the confidence intervals change by seasons, much as expected, they also shift position relative to other species by seasons. It is these changes in position relative to other species over different seasons which may be used to characterize species. Keith and Sanders (1990) used data for the winter season as it showed the highest correlation with species. The results here confirm this assertion, as the winter season shows the greatest variation in mean radiation and the confidence intervals are more separated.

Combining the different seasonal data with shifting confidence intervals holds promise as a technique to separate and, hence, classify eucalypt forests. While confidence intervals of some species may overlap for some seasons, they also separate in at least one of the seasons. For example, in the case of E. elata and E. globoidea (Figure 8), the confidence intervals overlap in summer but are different for the other seasons. One could derive a set of rules for each species which would specify the seasonal range in mean and variance of solar radiation so that, even if two species had the same range of total radiation, they could still be separated based on the difference occurring in one particular season.

Using raw plot data, there were significant differences


TABLE 4. STUDENT-NEWMAN-KEULS TEST RESULTS FOR SIGNIFICANT DIFFERENCES BETWEEN MEANS FOR THE VARIOUS SPECIES (USING ALL TREES). * INDICATES

SIGN~FICANCE AT a' = 0.05.

I Summer I

Winter Spring

between species, though the differences are not as significant as those obtained using the weighted data. If plot data are not weighted by the number of trees, then a plot with say 100 trees of E. sieberi has the same weight as a plot which has only one tree of E. sieberi, and both will contribute equally in the analysis. So if 20 plots were surveyed, there could be say ten plots with large numbers of E. sieberi at one end of the radiation range and there could be another handful of plots with say only one or two trees each at the other end of the radiation range. Because the plot data are not weighted, all plots contribute equally towards any statistical analysis and so the end result will not give a true indication of the environmental preferences of the species. It makes sense to weight the plots by the number of trees for each species because the presence of a large number of trees of a particular species in a plot indicates that the environment is more suitable compared with a plot having only a few trees. However, the frequency of occurrence (percentage)

PHOTOGRAMMETRIC ENGINEERING 81 REMOTE SENSING

of any species in a plot should increase with increasing environmental suitability. That the weighted as well as the unweighted (raw) data were both statistically significant can be explained by the forest being old growth; old growth is mostly dominated by fewer large trees.

A reasonable discrimination between species is obtained by using cluster analysis. Table 6 gives the results of K-Means clustering, with values in cells being the percentage of each species allocated to each cluster. The clustering was carried out on the basis of mean annual solar radiation received at the sites where species occurred. These results demonstrate that most of the species differentiate into particular radiation niches. For example, 65.4 percent of E. sieberi are allocated to cluster 2, 67.7 percent of E. maidenii to cluster 6,52.9 percent of E. elata to cluster 5, and so on. The results also highlight the similarit- ies between some of the species. E. sieberi and E. globoidea could be said to occupy similar radiation niches as 65.4 per-

Februarv 2000 201

TABLE 5. STUDENT-NEWMAN-KEULS TEST RESULTS FOR S~GN~F~CANT DIFFERENCES BETWEEN MEANS FOR THE VARIOUS SPECIES (USING MATURE TREES ONLY). * INDICATES SIGNIFICANCE AT a' = 0.05.

I Summer Autumn I

I Winter I I Spring I

TABLE 6. CROSSTABULATION SHOWING THE PERCENTAGE OF EACH SPECIES IN DIFFERENT CLUSTERS (USING ALL TREES)

Cluster Number

1 2 3 4 5 6 7 8

E. sieberi 4.7 65.4 3.7 7.0 14.2 5.0 E. muellerana 8.5 47.2 0.3 18.6 5.4 14.7 5.4 E. viminalis 10.7 50.7 21.3 1.3 16.0 E. cypellocarpa 6.8 52.7 7.7 15.0 13.0 4.8 E. agglomerata 11.2 27.0 10.7 28.8 17.6 4.7 E. obliqua 41.3 18.3 9.2 27.5 2.8 E. elata 20.6 20.6 52.9 5.9 E. smithii 7.2 26.1 1.4 17.4 2.9 15.9 E. globoidea 9.8 67.1 3.7 4.9 3.7 8.3 0.9 E. consideniana 47.4 1.7 1.7 6.4 E. bosistoana 12.9 45.2 41.9 E. maidenii 19.4 3.2 9.7 67.7

Note: Cell values show percentages; Empty cells indicate 0%


cent of E. sieberi and 67.1 percent of E. globoidea are allocated to the same cluster. This is consistent with earlier results relat- ing to positions of 95 percent confidence intervals for solar radiation for different species. In Figure 9, it was seen that the confidence intervals for mean solar radiation for E. sieberi and E. globoidea overlapped considerably in at least three of the seasons (autumn, winter, and spring). Based on this, it is reasonable to assume that both E. sieberi and E. globoidea prefer similar radiation environments, and the cluster analysis results confirm this.

Each species will exhibit its own particular response to a combination of environmental variables and so, while solar radiation may show a strong link with vegetation, it would be unwise to assume that solar radiation alone may be used to char- acterise species.

Possible Uses of Insolation Data in Forestry Modeling Because many species show that their radiation regimes are significantly different from other species, this information can be utilized to delineate individual species or to find the most likely habitats of the species. The different seasonal confidence intervals of means for each species can be combined to make the selection criteria. For example, from the means tables, the conditions for the different species would be used to

Habitat likelihood

00.. low high

Figure 10. Possible locations of E.sieberi based on seasonal insolation data.


Habitat likelihood . . law high

Figure 11. Possible locations of E.consideniana based on seasonal insolation data.

produce diagrams as given in Figures 10 and 11. Diagrams such as these can then be used for planning logging operations, habitat mapping, conservation work, etc. These figures do not confirm with 100 percent certainty that the particular eucalypt species will be found at the mapped site, but they pinpoint the most probable sites of occurrence and hence a good starting point. The Australian Koala Foundation is already using the insolation model for the prediction of koala populations. Koalas feed on specific species of eucalypts and, if the habitat of these species is mapped out, then the possible locations of koalas can be predicted. Solar radiation is used as one of the layers in the environmental model to predict the locations of these eucalypt species.

Similar work can be extended to forest types as well. If a large number of plots are surveyed and confidence intervals of means are produced, as done for the species distribution, then a similar set of conditions can be coded. This should show the most likely locations for the different forest types.

However, the main use of solar radiation in modeling eucalypt species would be as a value-added layer in other environmental models. As mentioned at the beginning of this paper, solar radiation is one of a number of environmental factors that affect species distribution. Other researchers (McColl, 1969; Austin et al., 1984; Mosley, 1989; Austin et al., 1990; Moore et al., 1991; Austin et al., 1994; Delaney, 1996) have shown the

February 2000 203

correlation between species a n d a number of other environmental factors. These factors c a n b e combined w i t h solar insolation t o model the species more effectively. In a GIS, each of these factors can be stored as a different layer a n d Boolean conditions can b e coded to model the different species. Solar radiation can also b e used a s a n input parameter in expert systems or neural networks.

Conclusion As solar radiation may influence plant growth indirectly through independent variables such as temperature and moisture content of soils, i t may b e considered a surrogate for a complex set of factors.

This research demonstrated that t h e distribution of eucalypt species over the s tudy area w a s related t o the seasonal solar radiation regime. There are significant differences in t h e radiation incident o n sites occupied b y different eucalypt species.

T h e a im has been t o develop a s imple method for estimat- ing the distribution of eucalypt species; a method which could be incorporated into other models to improve t h e overall classi- fication accuracy. T h e advantage of solar radiation a s a geographical variable i s that i t may b e quickly a n d cheaply calculated over a large area using only a digital elevation model a n d latitude a s inputs.

The research reported here also h a s important significance to remote sensing data interpretation. If different species occupy different topographic niches, then signatures wil l differ by species as wel l as slope and aspect. A n examination of t h e remote sensing data to determine if spectral signatures also correlate with species a n d solar radiation is currently under way.

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(Received 09 December 1996; revised and accepted 10 September 1998; revised 12 March 1999)


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Radiation-Vegetation Relationships in a Eucalyptus Forest€¦ · Lallt Kumar and Andrew K. Skldmore Abstract ... it will not work ade- quately in hilly regions where shading by topographic