Centre for Geo-Information, WUR, Wageningen
Thesis Report GIRS-2000-28-MB
Productivity-biodiversity patterns – a study using multitemporal Landsat TM NDVI data for the Alice Springs region, central Australia.
Maaike Bader November 2000
WAGENINGEN UR Productivity – biodiversity patterns in central Australia: Contents
Contents
FOREWORD...... 4
ABSTRACT ...... 6
1 INTRODUCTION...... 8
1.1 Biodiversity - productivity...... 8
1.2 Biodiversity – remote sensing...... 8
1.3 Productivity - remote sensing...... 9
1.4 Expected patterns ...... 10
2 METHODS ...... 12
2.1 Study area ...... 12
2.2 Image processing ...... 13 2.2.1 Data used ...... 13 2.2.2 Effect of atmospheric correction...... 13 2.2.3 Calculation of NDVI ...... 15 2.2.4 Spatial variation...... 17 2.2.5 Resampling to lower spatial resolution ...... 17
2.3 Fieldwork ...... 18
2.4 Statistical analysis...... 19
3 EXAMPLES OF VEGETATION TYPES AND THEIR NDVI SIGNAL ...... 22
3.1 Alice Springs ...... 22
3.2 Acacia scrubland ...... 24
3.3 Sand dunes ...... 25
3.4 Mountain ranges...... 26
3.5 Open grassland ...... 27
3.6 Spinifex...... 28
4 RESULTS ...... 29
4.1 Species...... 29
4.2 Class distinction in the field...... 29
4.3 NDVI values ...... 29 Productivity – biodiversity patterns in central Australia: Contents
4.4 Groundcover ...... 29
4.5 Comparing NDVI classes...... 30
4.6 Correlations species number – NDVI measures per plot and per transect...... 32
4.7 Correlations per landsystem...... 33
4.8 Spatial heterogeneity...... 34
4.9 Vegetation types...... 35
5 DISCUSSION...... 38
5.1 Vegetation cover ...... 38
5.2 Relationship productivity- species richness ...... 38
5.3 Species...... 40
5.4 Habitats for other taxa...... 41
5.5 Data characteristics...... 41 5.5.1 Influences on NDVI ...... 41 5.5.2 Spatial accuracy...... 42 5.5.3 Behaviour of variation...... 43 5.5.4 Measuring spatial variation ...... 43 5.5.5 Scale ...... 44
6 CONCLUSION & RECOMMENDATIONS...... 45
7 REFERENCES...... 46
APPENDIX 1...... 51
APPENDIX 2...... 55
APPENDIX 3...... 57
Productivity – biodiversity patterns in central Australia: Foreword
1 Foreword This report is the result of a 4-month thesis research at the Commonwealth Scientific and Industrial Research Organization (CSIRO) Centre for Arid Zone Research (CAZR) in Alice Springs, Australia. The thesis is part of my MSc courses in Geo- Information Science and Biology at the University of Wageningen, The Netherlands. The project ran from halfway July to halfway November 2000, and was supervised at CAZR by Graham Griffin, senior scientist, and supported from Wageningen by Steven de Jong, professor of Remote Sensing.
The supposedly arid desert of central Australia has been amazingly green this year. And now, halfway into November, there is no sign of the heat that I have been predicted for this time of the year. Instead there are cloudy skies, impressive thunderstorms and running rivers. I could rave on endlessly about the great mountain ranges, beautiful flowers and birds, cute wallabies, and wonderful weather of the Alice Springs region, but this will do.
I would like to thank my housemates, Lara, Shrike and Mel, for putting up with me. Many thanks to all staff at CAZR for support, advice and bush walks, and in particular Graham Griffin, for having confidence in what I was doing and helping out where necessary.
I dedicate this report to my friend Klaartje, who died in a car accident while I was away, and shouldn’t have.
Maaike Bader, Alice Springs 15-11-2000
Productivity – biodiversity patterns in central Australia
5 Productivity – biodiversity patterns in central Australia: Abstract
Abstract It is generally agreed that there is a relationship between productivity and biodiversity, and productivity can be measured by remote sensing, so it should be possible to use remote sensing for monitoring biodiversity. This idea was elaborated in a case study in arid central Australia, using a time-series of 9 Landsat Thematic Mapper (TM) images, and field data on species diversity of perennial plants. The Normalized Difference Vegetation Index (NDVI) was used as a measure for green vegetation cover, and the mean NDVI over the 9 images and temporal and spatial standard deviations (sd) were considered to reflect relevant aspects of productivity. Field samples consisted of species presence/absence data and vegetation cover estimates from transects of five 20-m radius circular plots. Field-data and remote sensing data were compared in three sets of analysis: 1. The field-estimated vegetation cover was compared to the NDVI measures; 2. The NDVI data was divided into classes, and the species richness compared between classes; and 3. The species numbers per plot, transect, vegetation type and landsystem were compared to the NDVI data for those units. Patterns of NDVI-classes could be well recognised in the vegetation in the field. Estimated total cover and mean NDVI correlated best, while sd NDVI was most positively correlated with cover of grasses and herbs. The classes of mean NDVI differed significantly in species richness, while sd classes did not. The pattern of NDVI vs. species numbers differs between landscape types. When using data per plots or transect all correlations are very weak. Vegetation types show a clearer pattern relating species numbers to mean NDVI as well as temporal and spatial sd. These relationships are roughly hump-shaped. The measure most important for species richness in landsystems is the spatial variation. The mean NDVI in a landsystem did not relate to species numbers. Mean NDVI and the temporal and spatial variation, as well as other environmental factors, interact to influence species numbers. The relationships between productivity and biodiversity and remote sensing data can be rather complex. Several factors can be responsible for disguising potential patterns, e.g. the measure for biodiversity, the spatial accuracy of the data, non-vegetation influences on the NDVI, the scale of variation, and the measures for productivity and persistence of productivity. Some suggestions are made for possible improvement.
6 Productivity – biodiversity patterns in central Australia
7 Productivity – biodiversity patterns in central Australia: Introduction
2 Introduction This study is based on two well-established ideas: 1. There is a relationship between productivity and temporal and spatial variability of productivity, and biodiversity (e.g. Pianka 1966, Ricklefs & Schluter 1993, Rosenzweig 1995); and 2. Productivity and temporal and its spatial variability are detectable by remote sensing (e.g. Huete 1988, Richardson & Everitt 1992, Clevers 1997). Adding up 1 and 2 logically leads to the conclusion: 3. Remote sensing can help to describe patterns of biodiversity. If this conclusion holds in practice, it would offer a great tool for monitoring biodiversity in remote areas, such as the arid inland of Australia. Whether productivity is actually the direct cause of the differences in species richness, or a co-variant, can be argued, but in either case, knowing how the two relate can be useful information for practical applications.
2.1 Biodiversity - productivity Most ecologists agree that there is some sort of relationship between species richness and primary productivity (e.g. Pianka 1966, Tilman 1982, Begon et al. 1990, Rosenzweig & Abramski 1993, Tilman & Pacala 1993, Wright et al. 1993, Scheiner & Rey-Benayas 1994, Rosenzweig 1995, Marrs et al. 1996, Jørgensen & Nøhr 1996, Ritchie & Olff 1999, Waide et al. 1999). The shape and nature of this relationship have not been agreed upon, however, and the ‘rules’, if there are any, may indeed have different effects in different ecosystems, for different groups of organisms and on different scales (Wright et al. 1993, Waide et al. 1999, Lawton 1999). The prevailing opinion is that the general shape is a unimodal one, and other shapes of relationships are simply a part of this universal shape, e.g. the increasing or decreasing phase, depending on the range of productivity sampled (Rosenzweig & Abramsky 1993, Begon et al. 1990). This is an interesting but safe theoretical assumption. The position of the optimum productivity for species richness is different for most types of species, ecosystems, observation scales and biogeographical regions. Therefore most relationships found can be explained using this model, which makes it a solid, though possibly not always realistic model. Different theories explaining the unimodal pattern and an evaluation are summarized in Rosenzweig & Abramski (1993). Apart from the total or average productivity, the spatial and temporal variation in productivity have also been linked to biodiversity, both in theoretical models (e.g. Tilman & Pacala 1993, Wright et al. 1993, Ritchie & Olff 2000) and based on observations (e.g. Scheiner & Rey-Benayas 1994, Gough et al. 1994, Pollock, Naiman & Hanley 1998). In most models, an increase in both spatial and temporal heterogeneity increases the possibilities for resource partitioning, thereby increasing the possibilities for species to co-exist, and hence biodiversity.
2.2 Biodiversity – remote sensing Several studies have used one or several of these factors to relate remote sensing data to ground data on species diversity (see also appendix 2). Jørgensen & Nøhr (1996) studied bird species diversity in the Sahel in relation to landscape diversity and biomass production, both derived from RS images. Fjelså et al. (1997) related ecoclimatic stability of African ecosystems, assessed by means of a 10-year RS time-series of a vegetation index, to the occurrence of biodiversity ‘hotspots: areas with high concentration of relict species and neo-endemics. Coops et al. (1998) used high-resolution aerial video data to predict habitat quality from spatial variation in the reflectance of Australian Eucalypt forests. In all of the studies above, the spectral domain is used directly through knowledge of the reflection characteristics of objects, e.g. the vegetation cover or canopy shadows. In many cases information is also derived from temporal and spatial patterns. In
8 Productivity – biodiversity patterns in central Australia: Introduction
some cases spatial patterns are in fact the only information used directly. Mack et al. (1997) used existing land-cover maps derived from Landsat TM images, and compared bird species numbers in woodland patches of different sizes. They found that the land cover map underestimated the size of the patches, and hence did not predict the number of species accurately.
Other studies have used the spectral RS data to classify their images by one of the traditional methods, and have compared the biodiversity between the distinguished classes. Nagendra & Gadgil (1999a) found that classes from supervised classification could distinguish landscape element types of the Western Ghats hills in India, and contained different species and had different species richness, but those from unsupervised classification did not. In a study of meadows in the Greater Yellowstone Ecosystem (USA), unsupervised classification was used in combination with manual merging of classes based on field knowledge of the vegetation. Here it was found that species distribution and species diversity did differ between classes (Debinski et al. 1999). A disadvantage of using general classification methods is, that no logical connection is established between the RS signal and the ground data. As Tuomisto (1998) put it: “The colour pattern in satellite imagery enable one to identify and map areas that differ in some way; field studies are then needed to find out whether these differences are significant in ecological and floristic terms”. A contrasting approach is well summarised by Stoms & Estes (1993): “… richness models could be developed that relate remotely-sensed data or indices to the underlying biophysical factors and then to the number of species”.
Classifying an image based on the expected meaning of spectral and/or temporal signatures, rather than just on their separability, allows for a different type of hypothesis testing. In this study, the hypothesis was that a relationship exists between the number of species and the productivity and persistence of productivity in an area. Two types of analysis were used to test this hypothesis. 1. The study area was divided into distinct classes of mean productivity (indicated by a vegetation index, as explained below) and variability of productivity, based on boundary values. These classes were compared mutually in terms of species diversity. 2. The species diversity and productivity parameters were compared between different units, defined by the sample units, the vegetation composition or the landscape type.
2.3 Productivity - remote sensing Productivity is the rate of conversion of biomass in a certain area in a certain amount of time. As such a rate is difficult to measure, various substitutes have been used in productivity-biodiversity studies, for instance biomass or aboveground biomass (e.g. Gough et al. 1994), rainfall (in Pianka 1966), actual or potential evapotranspiration (e.g. Hoffman et al. 1994), or ocean depth (e.g. Fraser & Currie 1996). In some theories productivity is also substituted with resources (Ritchie & Olff 1999) or energy (Wright et al. 1993). This different terminology could lead to some confusion. Hoffman et al. (1994) found a negative relationship between plant species richness and potential evapotranspiration (PET) in arid and semi-arid regions of southern Africa. They relate the PET to available energy, a relation that is technically true. However, the ‘energy’ as used in species-energy theory, should be more related to resources, and in arid environments thermal and solar energy are rarely the limiting resources. In these regions more evapotranspiration is more likely to have a negative effect on productivity by reducing the available moisture, and using it as a measure
9 Productivity – biodiversity patterns in central Australia: Introduction
for available energy to plants is therefore deceiving. For that reason rainfall is more commonly used as substitute for productivity in (semi-) arid regions.
Several measures related to productivity can be deduced from multi spectral remote sensing images. The result of productivity is the standing biomass, providing no major disturbances or grazing have taken place. Standing biomass is closely related to cover of green and inert vegetation, which has been successfully described in Central Australia by the ‘Perpendicular Distance band4-band 5’ (PD54) index, using Landsat Multispectral Scanner System (MSS) band 4 (500-600 nm) and band 5 (600- 700 nm) (Pickup et al. 1993). Channel 1 from the National Oceanographic and Atmospheric Administration – Advanced Very High Resolution Radiometer (NOAA AVHRR) has also been used as an indication of vegetation cover (Bastin et al. 1995). Plant productivity is directly related to the amount of photosynthesis taking place. Photosynthesis also causes the typical spectral properties of green vegetation, which are captured by optical remote sensing and used to calculate vegetation indices. Vegetation indices are broadly divided into ratio-based indices and orthogonal-based (or linear combination or n-space) indices (Clevers 1997, Huete 1988, Elvidge & Lyon 1985). The former are calculated by some ratio between the red (used for photosynthesis, low reflectance from green vegetation) and near infrared (NIR) (high reflectance from green vegetation) wavelengths, while the latter are based on the perpendicular distance to a soil line in a two or more band space. The index used in this study is a ratio-based index called the Normalized Difference Vegetation Index (NDVI). The NDVI has been, and still is extensively used for monitoring of green vegetation cover (e.g. Tucker et al. 1986, Hielkema et al. 1986, Nicholson et al. 1990, Chen & Brutsaert 1998, Teillet et al. 2000) all over the world, including central Australia (DPIF NT 2000, Environment Australia 2000, Foran & Pearce 1990). The combination of NDVI from multi temporal images has been used to calculate a measure of gross primary production known as Integrated NDVI (INDVI), or NDVI-days (e.g. Jørgensen & Nøhr 1996, Diallo et al. 1991, Prince 1991, Foran & Pearce 1990).
The aim of this study was to relate ‘productivity’ to biodiversity. The measures of productivity we were interested in were not the total production integrated over time, but the rate of production at different times, as well as the temporal variability in this rate. We chose to use the mean NDVI of 9 dates as a measure for the average production rate at a location, and the standard deviation from this mean as a measure of the variation.
2.4 Expected patterns At the regional scale at which this research was conducted, the expectation was to find a unimodal relationship between the productivity and richness in perennial plant species, even though the whole range of productivity is relatively low (Graham Griffin, pers.comm .). Where there is very little productivity, the circumstances are apparently too harsh for much plant growth, and only few species will be able to survive. These areas are dominated by ephemeral herbs and grasses, which only appear after good rains, when circumstances are more favourable. In the most productive areas, which in this environment are the rivers and drainage lines, competition becomes an important factor, and only a few arid-zone species are adapted to such circumstances. Intermediately productive areas will allow many species to survive physiologically, while not allowing heavy dominance of any species.
Spatial heterogeneity was expected to increase species number, but temporal variability was expected to act in a different way on the studied species: only long- term perennial plants were included, which have as a common trait their persistence
10 Productivity – biodiversity patterns in central Australia: Introduction
through hard times. They do not partition their environment in time, but they are dependent on a certain degree of persistence of a minimum level of resources. Temporal stability should therefore have a positive effect on perennial plants.
Talk about spatial scale can be confusing, as the terminology for map scale is the opposite for that of operational scale. In this paper a larger scale or higher scale level means a larger area and less detail, while a smaller scale or lower scale level means the reverse.
11 Productivity – biodiversity patterns in central Australia: Methods
3 Methods
3.1 Study area The study area is located in arid central Australia, and includes the town of Alice Springs (fig. 1). It was chosen so that the whole area was within reasonable travel distance from Alice Springs, but included a wide range of landscape types, including red sand dunes, hills and mountains of various geologies, rivers, low-relief calcareous areas and scrubland plains. It transverses the MacDonnell ranges from north to south, and includes a small part of the plains to the north of the ranges and a considerable area to the south, including the Waterhouse Ranges. The landscape types in the region have been classified into landsystems by Perry et al in1955 (Perry et al. 1961). These landsystems are based on a combination of geology, topography and vegetation, and Figure 1 Location of the study area (small have been widely used in land-use and box in center). environmental studies in the region. The landsystems found in the study-area are listed in table 1, and their distribution can be seen on plate 1. The climate of region is regarded as hot arid, with hot dry summers and cool dry winters. Rainfall is low and highly variable (Gentilli 1972). In the year 2000 unusually high rainfall has resulted in high cover of ephemeral herbs and grasses and extra growth and vigour in perennial plants. Images of similarly green years are included in the dataset. Table 1 Description of landsystem, after Perry et al. 1961. Code Name Description NM ‘Northern Combination of Alcoota, Boen and Bushy Park lndsystems. All plains N of Mulga’ MacDonnell ranges; weathered granite, gneiss or schist, or alluvial deposits; red earths; mulga in groves over short grass or woollybutt. Hm Hamilton Active alluvial fans. Plains flanking crystalline mountains, north of the MacDonnell Ranges; texture-contrast soils with short grasses or Scleroleana spp. or Maireana spp. (Chenopodiaceae), some red earths and red clay soils with mulga and short grass. Ha Harts Mountains of gneiss and granite. Uplands, steep-sided mountains, and hills, relief about 300 m; pockets of shallow gritty and stony soils; sparse shrubs and grasses. Bs Bond Springs Hills and plains of granite, gneiss or schist. Ridges up to 180 m high and rugged terrain with up to 30 m relief; some shallow gritty and stony soils; sparse shrubs and grasses. Narrow plains; various soils; sparse low trees over short grass. Gi Gillen Dissected ranges of folded sedimentary rocks, with summit planation. Quartzite and sandstone ridges up to 300 m high; little soil; spinifex. Vales with alluvial plains and gravel terraces; stony soils (texture-contrast, red earth), red clayey sands, and coarse soils; sparse shrubs and low trees, mulga, or witchetty bush over short grass. Td Todd Coalescent flood-plains of the Todd river and tributaries; sandy alluvial soils, some red clayey sands and silty fine, and layered alluvial soils; sparse low trees over short grass. Mu Muller Undulating terrain on mainly unweathered, folded sedimentary rocks. Low hilly or undulating, relief up to 25 m; calcareous earths; open or witchetty bush over short grass. (Nowadays more mulga than witchetty bush)
12 Productivity – biodiversity patterns in central Australia: Methods
Code Name Description Ew Ewaninga Undulating dune-covered terrain with stony conglomerate hills, relief up to 10 m; red dune sands; spinifex mainly under mulga. Si Simpson Parallel, reticulate, and irregular san dunes with stable flanks, minor areas of mobile sands; red dune sands and red clayey sands; spinifex. Kr Krichauff Sandstone mountains, not sampled.
3.2 Image processing
3.2.1 Data used Nine Landsat Thematic Mapper (TM) images of the same area (path 102, 77) were used, their recording dates spread out from February 1988 to March 1997 (fig. 4 and appendix 1). The images used were those available at CSIRO CAZR, were mostly cloud-free, and covered a wide range of circumstances, including high vegetation cover after rainfall events, and low cover in dry periods. The images were subsetted to 3 bands (2,3, 4) and the extent of the study area. Geometric registration was performed using existing ground control points (GCP’s) created by G. Pearce. I used UTM coordinates (zone 53 south) and the Australian Geodetic datum of 1984, even though this datum differs from that used for the GCP’s and most other spatial data in the area, the Astralian Geodetic of 1966 (AG-66), because the latter is not available in ENVI. However, as the two datums are only shifted relative to each other, not rotated or distorted, using the wrong datum did not cause problems in this case, and the output-image could be treated as a AG-66 referenced image and overlain with other AG-66 data. Output accuracy was generally less than a pixel. The software used for image processing was ENVI 3.1 (BSCLCC 1998). All overlay operations, visualisations and other GIS functions were performed in ArcView 3.2 (Esri 1999).
3.2.2 Effect of atmospheric correction. The Landsat tm images were taken at different times of the year and different times of the day, having different atmospheric conditions and sun angles. These factors can have different effects on NIR and red light, and thereby influence NDVI values (Huete & Tucker 1991). An atmospheric correction can minimize the differences in reflectance due to these different circumstances. It was investigated whether such a correction would be necessary for this application, looking at the effect of a correction on the NDVI values and differences between years. Because of the limited time available, only the simplest and quickest type of atmospheric correction was used, which is the dark pixel subtraction method. This method basically presumes that real data should be starting at 0 reflection, which is approximately the reflection from deep water or heavy shade. The histograms of the original pixel values (fig 2a) can be used to determine for each band the level at which real data starts, showing a marked increase from near-zero frequency. All values below this level are considered noise. By subtracting this value from all data in the corresponding band, the data are made to start at zero (fig 2b). The values subtracted from each band can be found in appendix 1, table 2.
13 Productivity – biodiversity patterns in central Australia: Methods
a.
b.
Figure 2 Histograms before (a.) and after (b.) dark pixel subtraction.
Another method for dark-pixel correction, subtraction of the minimum pixel-value, which is a default option in Envi software, was also tried out. This method was considered less suitable, because the value of one single pixel determines the value to be subtracted, making the method very sensitive to outliers and errors.
The NDVI values calculated from the images before and after atmospheric correction were compared for three images, to see if atmospheric correction would be likely to have an important effect on the outcome of the analysis. The histograms of the NDVI at different dates show that the NDVI values change considerably (fig 3). The range of NDVI values becomes larger for all dates. In June 1988 the green vegetation cover was high, which is reflected in the high NDVI values. After atmospheric correction these values increase even more. The NDVI values for February 1988 (before the rain, having low vegetation cover) are low and become even slightly lower after atmospheric correction. The low values of February 1990 increase slightly after the dark pixel subtraction. The irregular behaviour around NDVI zero, as seen in the histogram before correction, also seems to largely disappear afterwards. It is concluded that the changes in NDVI values are too large to be ignored, and that atmospheric correction is necessary for all images to be able to derive meaningful measures of the variation of NDVI over time.
14 Productivity – biodiversity patterns in central Australia: Methods
Figure 3 Histograms of the NDVI values in 3 of the images, before and after dark pixel subtraction.
3.2.3 Calculation of NDVI The Normalized Difference Vegetation Index (NDVI) is a widely used remote sensing indicator for green vegetation cover. It is based on the typical spectral reflection of green vegetation, which is very low in the red wavelengths because of energy- absorption by chlorophyll, and which is high in the near infrared (NIR). The NDVI and other vegetation indices can be influenced by several non-vegetation factors, such as soil-background, dead biomass and atmospheric conditions (but the latter have moslty been corrected for). An alternative method that could be successful for distinguishing green vegetation cover in this arid environment is spectral unmixing (Steven de Jong, Vanessa Chewings, pers.comm.). However, in order to use this method, the spectral signatures of endmembers need to be known, which is not the case for this area. Other vegetation indices are available, some of which correct for some of the non- vegetation influences (e.g. Huete 1988) (see Discussion; Influences on NDVI). However, the NDVI is the most wellknown vegetation index, and also used in the project this study is related to, which uses ready-made NOAA NDVI composites. Foran & Pearce (1990) found a good correlation between NDVI and total green cover in central Australia. As the observed patterns of NDVI in the images also did not indicate a problem, the NDVI was considered a suitable index. The NDVI is calculated by the following formula: (NIR-Red)/(NIR+Red), and was calculated from digital numbers (DN values). For Landsat TM this NIR corresponds with band 4, and Red with band 3. The dark pixel subtraction caused some pixels to have value 0 in TM band 3 or 4, resulting in NDVI values –1 and 1. These values are outside the normal NDVI range of the data (see fig 2) and were not realistic. Although only a very small part of the pixels would have this problem, the extreme values could have an influence by stretching the range of possible outcomes in later analysis. Therefore the standard ENVI function for calculating NDVI was replaced by a function that substituted 0 pixel values by 1 before calculating the NDVI, which would still yield a low NDVI value if NIR reflectance was low, and a high value if the red reflectance was low (appendix 1, fig 3 & box 1).
15 Productivity – biodiversity patterns in central Australia: Methods
Figure 4 Distributions of NDVI-values. Note the high values in June 1988, green after heavy rain. The NDVI values calculated for the images are very low, often below 0. This means that the DN values of the red wavelengths are often higher than those in the NIR, indicating a very low cover of green vegetation for most dates. According to Foran & Pearce (1990), NDVI values in Central Australia typically range between –0.2 and 0.7. Compared to this range, the values found here are rather low. The lowest values, in December 1994, correspond to a dry period with very low vegetation cover, which may have caused NDVI values below the typical range. Values of 0.7 are very rare in these data, but do occur.
In the variable arid environment of central Australia, the green vegetation cover is usually rather low, but also very variable. The average greenness and the variability of this greenness over time are expected to be able to distinguish between some of the different vegetation types in the area, and to be correlated to species richness. The mean and standard deviation of the NDVI on the different dates were calculated to create an image of relative measures of average and variability of green vegetation cover.
The following functions were used for calculating mean and standard deviation of NDVI over time: mean = ( Σbi)/9, stdev = ( Σ(bi-b10)^2)/9 Where bi = band i, and i = 1, 2, …9, and b10 = band 10 Bands 1 to band 9 are the NDVI images of different dates. Band 10 is the average (from first formula).
One of the images, that of March 1995, had a small cloud on it. The NDVI values of the cloud should of course not be used in the calculation of mean and standard deviation. The mean and stdev were also calculated excluding March 1995, and these were assigned to the cloud area using a mask, while the rest of the scene has the 9-date measures.
The ranges of values of mean and temporal standard deviation of the NDVI were divided into 5 and 4 classes respectively. The classes were based on the histograms of the two measures, and boundaries were chosen so that each class was represented by a reasonable number of pixels, and represented a specific range (e.g. ‘very low’, or ‘medium’) compared to the total range (fig 5). The classes were used throughout the study for selecting sampling sites as well as statistical analysis. A
16 Productivity – biodiversity patterns in central Australia: Methods
poster-size map was produced based on these classes (larger version of plate 2) and including roads, which was used during fieldwork.
Figure 5 Histograms of mean and SD NDVI calculated from 9 images. The frequency is the number of pixels. Lines indicate the class-boundaries.
3.2.4 Spatial variation The spatial variation in mean NDVI was expressed as the standard deviation (sd or stdev) in moving windows of different sizes (3x3, 7x7, 17x17 and 33x33 pixels) (plate 3 and 4). This measure was chosen mainly because it is easy to implement, and gives a usable result. More elaborate methods, such as variograms (see discussion), may have given more information, but are not readily available in all image- processing software, at least not in ENVI. Time limitations did not allow for manual programming of such functions.
An alternative measure for variability that is also easy to calculate, the coefficient of variation, has been used to measure temporal variation in NDVI in various studies (Chen & Brutsaert 1998, Fjeldså et al. 1996, Eidenshink & Haas 1992, Tucker et al. 1991), although the standard deviation and variance have also been used for temporal (Peters et al. 1993, Tucker et al. 1991) and spatial (Coops et al. 1998) variation. To test the effect of using a different measure of variation, I replaced the stdev by the unbiased coefficient of variance (Sokal & Rohlf 1995), adding 1 to the mean NDVI values to avoid using negative values.
3.2.5 Resampling to lower spatial resolution Originally the intention was to compare RS imagery of different resolutions (Landsat TM (30x30 m) and MSS (100x100 m) and NOAA AVHRR (1.1x1.1 km) data), but logistical difficulties did not allow this. Instead the TM images were resampled to a 100x100 m resolution and compared to the original TM image. The resampled image is similar to a MSS image, except that the spatial accuracy is higher in the TM derived image, and the bands are a little bit different. Resampling to NOAA AVHRR resolution was not tried out, but considering the observed scale of vegetation patterns, this resolution could most probably not distinguish between vegetation types and local differences in vegetation greenness, or be related to local species numbers. In a study of species richness in all of inland Australia, temporal NDVI signatures of 4x4 km NOAA AVHRR composites are being used. In that study compared to this one, not only the spatial scale changes, but so will the range of values represented, one might say the ‘data-scale’ or extent (Waide et al. 1999). It will be interesting to see what relationships are found at this coarse scale (Graham Griffin pers. comm.). 3.2.6
17 Productivity – biodiversity patterns in central Australia: Methods
Using the TM data at high spatial resolution allows small objects to be distinguished, but also has some disadvantages. When studying large areas, the large file-size of detailed data slows down processing considerably. Also, the spatial accuracy of the field-data is such, that they may be compared to the wrong pixel-value, especially if the pixels are small. Merging the 30-m TM data to larger pixels reduces the file-size, and produces a larger accuracy of field-data appearing in the right pixels. However, some detail is also lost, so that for instance small drainage lines may be indiscernible. The small field-plots cannot represent the much larger pixel-areas (or ‘ground resolution elements, Atkinson 1997). This means that the field-data also need to be merged to produce comparable scales in both datasets. For 100x100-m resampled images the transect data were used, rather than the plot- data (see fieldwork methods), because the 5 plots, each with 20-m radius, in each transect have a support-area (5*2*π*20 = 628m 2) more similar to the pixel size (10.000 m 2). Because the transects are linear and about 200 m long, they do not fall within 100-m pixels. They are likely to cover more variation than a square area would have, and the species number would therefore be an over-estimation of the number of species. However, the area is smaller than that of a pixel, which would produce an under-estimation. Both the over- and under-estimation will be greater in the more heterogeneous areas, but the latter will be stronger in areas with small-scale variation (within 100x100-m areas), while the latter depends on variation at a slightly higher level (between 100x100-m areas). Also the shape of the spatial patterns is important – linear patterns such as longitudinal dunes or surfacing tilted geological layers, both common in parts of the study area, could cause lower variation in long transects than in square areas, depending on the direction of the transect. This complex interaction of under- and over-estimations was presumed to average out, and not further considered.
The red and the first NIR band of the TM-images (band 3 and 4) were resampled to a 100x100-m resolution, and the NDVI recalculated. The resampling was done with the original DN values, because this average could simulate data from lower resolution remote sensors, for instance 100x100-m Landsat MSS data. Simply averaging the NDVI values would not have produced the same result. This contrasts with the temporal averaging procedure, where NDVI values were used. A recalculation of the NDVI from average DN values would have been meaningless in this case. The resampling did not significantly change the result of any of the analysis, which could lead to the conclusion that the MSS resolution is as suitable for relating biodiversity at these scales to NDVI from RS as TM data is. In that case it would be preferable to use MSS data, because of the lower cost, storage space and computation time.
3.3 Fieldwork Two separate surveys provided the ground data for this study. Both consisted of transects of 20-m radius plots, within which the presence of long-term perennial plant species was recorded. Annual and biannual species were excluded, because these are very variable in time. Most annuals are ephemeral herbs and grasses that only appear after good rains. Many do show preferences for certain habitats, but they were not expected to respond strongly to productivity patterns, because they are vegetatively absent during low-productivity times (but see discussion). Some species are short-term perennials under favourable circumstances, but these were also excluded, for the same reasons. Species were either identified in the field, or collected for later identification (Urban 1990, nomenclature from Albrecht et al. 1997) The first survey was conducted in 1995, and concentrated on the mountain ranges. It was aimed at collecting data for constructing species occurrence models based on environmental variables such as substrate and hydrology. Transects were 1 km long,
18 Productivity – biodiversity patterns in central Australia: Methods
with 50 m between the centres of the individual plots. They were positioned within geological strata (rock types), but laid out to cover a large range of elevations within the strata (Griffin 1997a). The second survey was carried out in August and September of 2000, and included some landscape types not surveyed in 1995, such as sand dune areas and low relief scrubland plains, as well as some more samples in the mountainous areas. This survey was conducted specifically for this study, and the location of transects was based on a map of mean and stdev NDVI classes. Transects were chosen to fall within a combination of mean and stdev NDVI classes, and were located within reasonable walking distance from roads and tracks. Transects consisted of 5 plots, with their centres 60 m apart. Apart from species presence, an estimation of the ground cover was recorded. Grasses, herbs, low and high shrubs, and trees were recorded separately, in 6 cover-classes (<5%, 5-20%, 20-40%, 40-60%, 60-80%, 80- 100%). The positions of the first and last plot were recorded using an autonomous handheld GPS, and the positions of the plots in between were derived from these points. The positional accuracy of the GPS was up to 100m in 1995 (low due to selective availability scramble), while in 2000 accuracy was usually between 10 and 20 m.
3.4 Statistical analysis. Species presence was recorded per plot, and species numbers were calculated per plot, per transect and per landsystem. A species area curve per landsystem showed that the sample sizes per landsystem were too small, as well as too unequal, to give a good estimate of the total number of species per landsystem. Also the total number of species encountered in a landsystem was positively correlated to the number of plots sampled. Therefore only the number of species in the first 125 plots per landsystem was used as a comparative measure. Only landsystems where 125 plots or more had been sampled were included for the analysis at landsystem level.
The field-data were combined with the image data, by overlaying the plot positions on the images in ArcView. A digital version of the 1:1,000,000 landsystem map (Perry et al. 1961) was used to stratify the data for some of the analysis. Microsoft Access was used for further database management, MS Excel for data- entering and other spreadsheet functions and Systat 9 (SPSS 1998) for statistical analysis.
Analyses were conducted for several levels of spatial and/or organizational scales of field-data and remote sensing data. Corresponding to the plot-scale were the temporal mean and standard deviation of NDVI values in the 30x30-m Landsat TM pixels. Transect-data were compared to the average and variation of the mean temporal NDVI, as well as the mean of the temporal stdev in 100x100-m resampled pixels, or to averages of the 30x30-m pixels corresponding to the plots of a transect. Summaries of species numbers and NDVI measures were used to compare different vegetation types (community level, Waide et al 1999) and landsystems (landscape scale, Waide et al 1999). Also the NDVI classes were compared mutually. Correlation probabilities are all computed with consideration for the increased chance of finding a correlation with multiple comparisons, using Bonferroni probabilities (see Legendre & Legendre 1998). 3.4.1.1 Vegetation cover The relationship between vegetation cover and NDVI was tested by correlating the cover as estimated in the field with the NDVI from the TM images. Several cover- measures were derived from the cover-classes of different vegetation components. 1. Grass+herb cover was defined as: 5 * (grass cover + herb cover) / (grass cover + herb cover + spinifex cover + bare ground + rocks). This formula was used to reduce the effect of differences within cover classes. For instance, if there was
19 Productivity – biodiversity patterns in central Australia: Methods
33% cover of grass (cover class 2) and 33% herbs (2), and 34% bare ground (2), then ground cover is 5*4/6 = 3.3. If herbs and grasses are 22% (both 2), bare ground is 56% (3), and ground cover is 5*4/7 = 2.9. 2. Ground cover = 5 * (grass cover + herb cover + spinifex cover) / (grass cover + herb cover + spinifex cover + bare ground + rocks). 3. Shrubs and trees cover = low shrub cover + high shrub cover + tree cover 4. Cover of perennials = shrubs and trees cover + spinifex cover 5. Total cover = ground cover + shrubs and trees cover 6. Total unvegetated = bare soil + rocks. 7. Spinifex cover was also tested separately, but as a vegetation type rather than a cover class. 3.4.1.2 Species richness vs. mean NDVI and temporal variation. An analysis of variance (ANOVA) was carried out to compare the classes of mean and stdev NDVI and their interaction. Landsystems were also included in an ANOVA, to see whether NDVI classes behaved differently in different landscapes. Bar graphs of the species richness per class of combination of classes were used to see the nature of any differences. Correlations were calculated between species numbers per plot, per transect and per landsystem, and mean NDVI, stdev NDVI, and spatial variation at different scales. 3.4.1.3 Spatial variation Spatial heterogeneity has often been found to correlate positively with species richness (Pollock et al 1998, Wright et al 1993). The relationship between spatial heterogeneity and species richness was investigated at several scale-levels. Spatial heterogeneity was expressed as the standard deviation of the NDVI (the temporal mean) in environments of different sizes: 90x90-m, 210x210-m, 510x510-m and 990x990-m and landsystems. The total variation between pixels per landsystem however, was not independent from the size of the landsystem, so that we used the average of the spatial variation in the different sized windows rather than the total variation.
Species numbers per plot and per transect were compared to the spatial heterogeneity in their surroundings. It was also expected, that higher spatial variability between pixels would increase the number of species per transect compared to the numbers per plot. The ratio of species number per plot and species number per transect was tested for correlation with the spatial variation. At a higher scale level, the number of species in a landsystem was compared to the mean and temporal and spatial variation of NDVI values in that landsystem. 3.4.1.4 Vegetation types The species presence in the plots was combined to derive the frequency of species occurrence per transect (5 plots per transect). These frequency data were used to cluster transects into groups representing vegetation types. The clustering was done in the pattern analysis package PATN, and based on the Bray & Curtis association measure (Belbin1995). A dissimilarity matrix is calculated, and based on that the samples are grouped. The level at which the groups are returned, and hence the number of groups, can be adjusted. The 400 transects were classified into 20 classes, some of which were manually merged. Merged groups were those consisting of few transects and all of those on grass/herb areas – these all had very few perennial species, separating them out too easily. Based on vegetation structure and the main constituting species, a vegetation class was also assigned to each transect based on direct field observation during the second survey period. These classes were compared with the calculated groups, which, together with an inspection of the species encountered in each group, allowed for interpretation and manual merging of the groups. Groups not represented in the second survey were not merged.
20 Productivity – biodiversity patterns in central Australia: Methods
An ANOVA was performed to assess the significance of the differences in NDVI signals and species numbers between vegetation groups. Bar graphs ordered by different measures were used for examining the shapes of their relationship
21 Productivity – biodiversity patterns in central Australia: Examples
4 Examples of vegetation types and their NDVI signal
4.1 Alice Springs The town of Alice Springs, the only town in the study area, offers a good example to explain the legend of the NDVI-classes map (see below). The town shopping and business centre shows up as being a dark red. This built-up area is never very productive in terms of vegetation growth. Around the centre there are various residential areas. Gardens are persistently watered, resulting in dark shades of green. The pink and yellow pattern around the town corresponds to gneiss hills with sparse shrubs. The linear feature south of town is a quartzite ridge, which is part of the Heavytree range. The Todd River is another feature that readily recognized. It cuts through the Heavytree ridge at Heavytree gap, and continues south from there with a wide bare riverbed and green banks. Some other human influences can be seen in the location of the rubbish dump, showing up bare with little variation, and the area of sewage ponds, very variable but on average quite productive thanks to extra inputs.
Alice Springs
Town centre
Residential areas
Heavytree Gap Heavytree range
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Rubbish dump
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5 km Mean NDVI 12 22 32 42 52
14 24 34 44 54 16 26 36 46 56
SD NDVI 18 28 38 48 58
22 Productivity – biodiversity patterns in central Australia: Examples
Rivers This image shows the Hugh River running through low relief country (top, landsystem Mu) and cutting through a range of quartzite and sandstone ridges (photograph, landsystem Gi). The river shows up clearly as being very green. The dark green indicates that temporal variability in greenness is limited. This can be attributed to the presence of river red gum ( Eucalyptus camaldulensis ), which is a large tree, resistant to the forces of heavy floods, that can get water from underground sources even in dry times. The river has several plaiting channels in most places, causing an alternation of green banks with trees and the actual channels, which generally consist of bare whitish sand and pebbles, and floodplains with grass-cover, both of which show up as having lower greenness values.
Photograph: the Hugh River entering the Waterhouse Ranges. Trees are river red gums.
5 km
Mean NDVI 12 22 32 42 52
14 24 34 44 54 16 36 26 46 56 SD NDVI 18 28 38 48 58
23 Productivity – biodiversity patterns in central Australia: Examples
4.2 Acacia scrubland Acacia scrubland is a very common vegetation type in the study area. It nearly always shows up as mean-NDVI class 4, being relatively green. The temporal variability is relatively low. The photograph shows a mulga ( Acacia aneura )-dominated scrubland. This is by far the most common Acacia scrubland encountered in the area, followed at some distance by witchetty bush ( Acacia kempeana ). The soil was covered with herbs and grasses when this photo was taken. This cover is much lower in dry periods (Melinda Hillery, pers. comm .).
1 km
Mean NDVI 12 22 32 42 52 14 24 34 44 54
16 26 36 46 56
SD NDVI 18 28 38 48 58
24 Productivity – biodiversity patterns in central Australia: Examples
4.3 Sand dunes The south of the study area is dominated by red sand dunes. These are mostly old inactive dunes covered in spinifex ( Triodia basedowii and Triodia pungens ). NDVI levels are intermediate here, and have relatively low spatial and temporal variability. The Central Australia Railway is clearly visible running North-South.
1 km
Mean NDVI 12 22 32 42 52 14 24 34 44 54
16 26 36 46 56
SD NDVI 18 28 38 48 58
25 Productivity – biodiversity patterns in central Australia: Examples
4.4 Mountain ranges Many mountains in the area have a clear structure of tilted geological layers, resulting in linear shapes. The Waterhouse range (bottom image) consists of two ranges of the same resistant quartzite layers, surrounding a valley cut in more erodable rock types. The alternation of layers of different rock-types is reflected in a similar pattern in the vegetation, which makes the fold-structure clearly visible on the image. The high parts of the Waterhouse Range are quite well vegetated with Acacia scrubland (see photograph), while the valley, has mostly open grassland and spinifex ( Triodia brizioides ) on dolomite, both giving low NDVI values. Drainage systems go west and east, and have a species-rich high scrubland vegetation. The northern part of the MacDonnell Ranges (top image) consists mainly of hills and mountains of high-grade metamorphic rocks including gneiss and granite (Griffin & Tier 1997b). The structures in this area are less obvious. The vegetation cover is mostly open mixed scrubland and is usually low due to the rocky substrate and drought. Variability is high on a small scale.
5 km
Mean NDVI 12 22 32 42 52 14 24 34 44 54 SD 16 36 NDVI 26 46 56 18 28 38 48 58
26 Productivity – biodiversity patterns in central Australia: Examples
4.5 Open grassland Open grassland areas have low NDVI values, although not the lowest possible. In good years like 2000, they can have full vegetation cover (top photograph), but at other times these areas may be completely bare (small photograph). When bare, the red soils of most of these areas may produce a false greenness-signal (Huete & Jackson 1987 + see discussion). This may cause an increase in the mean NDVI and a decrease in the variability. In some grassland areas there are also some dispersed trees or shrubs. These seem to have little influence on the NDVI values, but do increase the number of perennial species recorded.
Mean NDVI 12 22 32 42 52 14 24 34 44 54 16 26 36 46 56
SD NDVI 18 28 38 48 58
27 Productivity – biodiversity patterns in central Australia: Examples
4.6 Spinifex Several types of spinifex-vegetation are found in the study area, some having quite distinct NDVI responses. Spinifex on sand dunes typically has intermediate NDVI values (see example above). A spinifex species encountered in most parts of the study area, is Triodia brizioides , which grows on dolomite or limestone rock outcrops and hills. This vegetation type has the lowest NDVI values of all. At the time of the second survey, the spinifex was all flowering, resulting in view not unlike a field of cereal, and certainly not very bare looking. The actual ground cover of the tussocks was around 50% in most areas. Three factors may explain the low NDVI values. The typical substrate of this vegetation has much less colour than most other rocks and soils, which would give it less false greenness as compared to the spinifex on red sand and to the grassland areas (Huete & Jackson 1987). An adaptation of spinifex to the arid environment is having a very small leaf-surface, to reduce water-loss. Also, the tussocks are long-lived and contain rather a large amount of dead biomass. This fraction will probably increase during dry periods. Both these characteristics can reduce the NDVI (see discussion). These vegetations contain a variable, but relatively high number of other perennial species. The photograph shows a Triodia brizioides area in the foreground, Acacia scrubland in the distance, and the northern edge of the MacDonnell ranges.
Mean NDVI 12 22 32 42 52 14 24 34 44 54 16 26 36 46 56
SD NDVI 18 28 38 48 58
28 Productivity – biodiversity patterns in central Australia: Results
5 Results
5.1 Species. In total, 94 perennial plant species were found and included in the surveys. The main plant families represented were the Mimosaceae ( Acacia species), Caesalpinaceae (Senna species), Proteaceae ( Grevillea and Hakea ), Myoporaceae ( Eremophila species), Myrtaceae ( Eucalypts and tea tree), and Gramineae ( Triodia species) (see appendix 3 for complete species list). Vegetation types encountered ranged from open areas with annual herbs and grasses to floodplain forests, with different types of scrubland in between, as well as at several distinct spinifex communities.
5.2 Class distinction in the field In the field the patterns on the NDVI-classes map could be recognised very well in the vegetation, especially outside the MacDonnell ranges, because in the mountains patterns are finer. Outside the mountains, the lowest NDVI values corresponded with dolomite outcrops areas with spinifex ( Triodia brizioides ) occurring in many landsystems. The next class up were areas with only grass and herb cover, a lot of which is annual growth, and sometimes some widely dispersed trees or shrubs. Medium NDVI values mostly corresponded with spinifex vegetations on red sandy soils, and with Senna and some mixed low scrublands. The fourth mean-NDVI class was typical for Acacia -scrublands dominated by mulga ( Acacia aneura ) and witchetty bush ( A. kempeana ), sometimes combined with other high shrubs. The highest NDVI class was found in rivers, the outflow of the Todd River and local drainage areas, and usually indicated either river red gums ( Eucalyptus camaldulensis ) or a mix of high trees and lush undergrowth of (introduced) grasses (but these grasses may look very different in less favourable years).
5.3 NDVI values The NDVI values used in the analysis were not the actual NDVI values as computed in ENVI (e.g. compare fig 5 and fig 6). Something went wrong either during stretching of the NDVI values to integer values between 0 and 255 in ENVI (done to be able to transform the file to an ArcView *.bil file), or with the conversion from ENVI to ArcView. Although the stretch owas a linear one changing the data from –1 to 1, into 0 to 255, the data obtained from ArcView did not return the same values when ‘destretched’ using the opposite formula (x/127.5 – 1). Relative to each other, the values are still valid however, so they can be used for relative comparison without problems.
5.4 Groundcover The relationships between 12 vegetation cover and NDVI 10 values in plots are shown in 8 table 2. Mean NDVI has the best correlation with total 6 vegetation cover (fig 6), while 4 NDVI stdev is best correlated to the cover of grass and 2 herbs, the cover type notably vegetationTotal cover 0 most variable. Perennial cover -0.4 -0.3 -0.2 -0.1 0 0.1 0.2 0.3 0.4 is not correlated to NDVI Mean NDVI variation, positively or negatively. Unvegetated Figure 6 Relation between the total vegetation cover, ground is negatively correlated estimated from field observations, and the mean NDVI from 9 Landsat TM images (r=0.570).
29 Productivity – biodiversity patterns in central Australia: Results
with both NDVI measures. Including spinifex in the ground cover measure decreases correlation coefficients with NDVI – probably as an effect of its low NDVI signal and longevity, as opposed to annual herbs and grasses. Species richness is best correlated with cover of perennials (shrubs, trees and spinifex). The cover of perennials and that of herbs and grasses are negatively correlated (r= -0.367, p<0.001), which offers an explanation for the negative correlation of herbs and grasses with species numbers. Table 2 Spearman correlation matrix. Correlation coefficients of cover measures with species numbers and NDVI measures per plot (n = 916). * = p<0.001.
Grass+herbs Ground Shrubs+trees Perennial Total cover cover cover cover Total cover unvegetated Species per plot -0.184 * -0.169* 0.463 * 0.487 * 0.360 * 0.088 Mean NDVI 0.349 * 0.234 * 0.492 * 0.280 * 0.570 * -0.304* Stdev NDVI 0.399 * 0.319 * 0.228 * 0.040 0.373 * -0.357*
5.5 Comparing NDVI classes The assumptions of a linear model are sufficiently met by the data. A Levene test showed that variances are equal between classes of mean and stdev NDVI. The distributions differ significantly from normal according to a one-sample Kolmochorov- Smirnov test, but their histograms show that the deviation is not very large. The classes based on mean NDVI show significant differences in species richness (p<0.001) (fig.7a).
5.5 5.5
ab a 5 5 a a b b 4.5 4.5 a a
4 4 # species per plot per species # c plot per species #
3.5 3.5 1 2 3 4 5 1 2 3 4 Mean NDVI class a. b. Stdev NDVI class
Figure 7 Average species number per plot for each class of mean NDVI (a) and standard deviation of NDVI (b) over 9 dates. Error bars represent the standard error of mean. Corresponding letters indicate bars that do not differ significantly.
The classes of temporal variation in NDVI do not differ significantly (p>0.01) (fig 7b), but combined with the mean NDVI, they do contribute to differences in the number of species. Species richness is highest at very low and intermediate mean-NDVI. The combined classes show that these patterns are interacting as well (fig 9).
The different landsystems also have significantly different species numbers. The pattern of mean and stdev NDVI also differs between landsystems. This is obvious from fig. 9, and reflected in the high F-value (46.34) for the significant (p<0.001) interaction. For instance, in the Gillen landsystem (see table 1) the number of species is nearly the same for each class of mean NDVI, while in the Ewaninga and Todd landsystems
30 Productivity – biodiversity patterns in central Australia: Results
it increases monotonically. In the Bs = Bond Springs : gneiss hills Simpson landsystem and in the Ew = Ewaninga : red sand dunes + conglomerate hills Northern Mulga, there is an optimum Gi = Gillen : quartzite & sandstone ridges at intermediate levels, which is also Ha = Harts : gneiss mountains true in the Muller and Hamilton Hm = Hamilton: plains flanking MacDonnell ranges in N systems, except that these have Mu = Muller : calcareous, low relief extra high species richness at very NM = ‘Northern Mulga’: plains N of MacDonnell ranges low mean-NDVI (fig 8). A similar Si = Simpson : red sand dunes heterogeneity is seen in the stdev Td = Todd : floodplains of Todd river classes.
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Figure 8 Mean number of species per plot per mean-NDVI class for each landsystem. Bars represent standard error.
31 Productivity – biodiversity patterns in central Australia: Results
Table 2 Summary of ANOVA outcomes comparing NDVI classes, at 2 image resolutions. Two-way ANOVAs, using mean NDVI classes and stdev NDVI classes, and species per transect, only returned the outcome for the total model, not per factor – for these tests the value given for mean+ stdev + interaction is that for the factors together. Landsystems were also included in other ANOVA models – these models and interactions were significant but are not shown in the table.
No of species ANOVA-results per transect No of species per plot Mean NDVI 30-m pixels F-ratio 6.479 12.957 P 0.000 0.000 Stdev NDVI 30-m pixels F-ratio 2.402 3.828 P 0.067 0.010 Model: Mean + Stdev + F-ratio p interaction in 30-m pixels F-ratio 2.699 Mean 8.757 0.000 Stdev 4.828 0.002 P 0.000 Interaction 4.713 0.000 Mean NDVI 100-m pixels F-ratio 5.446 11.686 P 0.000 0.000 Stdev NDVI 100-m pixels F-ratio 1.957 4.026 P 0.120 0.007 F-ratio p Model: Mean + Stdev + Mean 8.689 0.000 interaction in 100-m pixels F-ratio 2.802 Stdev 1.757 0.153 P 0.000 Interaction 3.673 0.000
5 # # species
0 Stdev class: 1 2 3 4 1 2 3 4 1 2 3 4 1 2 3 4 1 2 3 4 Mean class: 1 2 3 4 5 Figure 9 Average species number per plot for a combination of the classes of mean NDVI and those of standard deviation of NDVI over 9 dates. Error bars represent the standard error of mean.
5.6 Correlations species number – NDVI measures per plot and per transect The correlation between the mean NDVI in 30 m pixels and the number of species per plot is significant (p<0.001), but the correlation coefficient (r) is very low (r= - 0.109) (fig 10). There is no significant correlation between stdev NDVI and species richness per plot. The number of species per transect is also negatively correlated to the mean NDVI (average of all plots in a transect), and not significantly correlated
32 Productivity – biodiversity patterns in central Australia: Results
with the temporal variation. 16 The average number of 14 species per plot per 12 transect, is not correlated to 10 mean or stdev NDVI. The 8 number of species per plot 6 was correlated more 4 strongly to the average 2 NDVI and temporal plot per species # variation of the landsystem 0 -0.4 -0.2 0 0.2 0.4 within which it was situated mean NDVI than to the local values (table 3). Figure 10 Relation between the mean NDVI and the number These weak correlations of species per plot. The highest NDVI values are those from can be due to there being river vegetations – these usually consist of only 1 to 4 species no relationship, to other (river gums, tea tree and some temporary stray species). factors or interactions disguising the relationship, or to the shape of the relationship. Other tests show that there is a relationship, but it is not linear. Within each landsystems the correlations are different, but never very strong. The strongest correlation was found in the Simpson landsystem, between species number and stdev NDVI (-0.391).
Table 3 Correlation coefficients and significance for NDVI values at different image resolutions and at different field-data scales. Numbers between brackets are the number of comparisons the significance is based on, correcting with Bonferroni method. No of No of species No of species per per plot per species per transect transect plot Mean NDVI 30-m pixels r -0.207 -0.146 -0.109 p 0.001 (8) 0.090 (8) 0.000 (4) Stdev NDVI 30-m pixels r -0.035 -0.028 -0.031 p 1.000 (8) 1.000 (8) 1.000 (4) Mean NDVI 100-m pixels r -0.233 -0.175 -0.134 p 0.000 (11) 0.012 (11) 0.000 (11) Stdev NDVI 100-m pixels r -0.024 -0.013 -0.01 p 1.000 (8) 1.000 (8) 1.000 (8) Mean NDVI in landsystem r - - -0.323 p - - 0.000 (7) Mean temporal stdev in landsystem r - - 0.08 p - - 0.006 (7)
5.7 Correlations per landsystem Fig 11 shows the relationship between the number of species in 125 plots in a landsystem and various NDVI-related properties of these landsystems. Correlations were not significant, but the patterns observed indicate a positive relationship between spatial variability and the number of species, but no relationship between species richness and mean NDVI.
33 Productivity – biodiversity patterns in central Australia: Results
Removing one landsystem, the Todd River floodplains, rises the Pearson r from 0.49 to 0.86, and the Spearman r from 0.43 to 0.94, making the correlations significant. Leaving this landsystem out can be justified by the aberrant nature of its spatial variability – it represents a temporal and spatial change from green to very green – a large difference in NDVI, but not in the conditions for perennial plants. However, the large effect of leaving one landsystem out does also show the instability of the correlation.
50 50 45 Gillen 45 Gillen 40 40 35 35 Todd Todd 30 # species # #species 30 25 Simpson 25 Simpson 20 20 0.01 0.015 0.02 0.025 0.03 0.035 0.05 0.10.10.1 0.15 a. Sd NDVI in 30x30-m window b. Sd NDVI in landsystem a.
50 50 45 Gillen 45 Gillen 40 40 35 35 Simpson Todd Todd
# species 30 # species 30 Simpson 25 25 20 20 0.02 0.04 0.06 0.08 0.1 0.12 -0.2 -0.15 -0.1 -0.05 0 c. Sd NDVI in 1x1-km window d. Mean NDVI a. a. Figure 11 Relation between the total number of species in 125 plots per landsystem and the spatial variation of the NDVI in 90x90-m areas (a) and 990x990-m areas (b), temporal variation of the NDVI (c), and the mean NDVI (d), all mean values per landsystem. The Todd landsystem is the very productive river floodplain.
5.8 Spatial heterogeneity Spatial heterogeneity has not been shown to increase species numbers in plots or transects. The number of species in a transect and the average number of species in each plot of the transect, did not correlate with spatial variation in NDVI in the tested environment-sizes. The spatial variation in 90x90-m or 210x210-m windows, areas comparable to the size and scale of a transect, did not increase their number of species, nor did it shift the ratio between the two measures of species richness, the numbers per plot and those per transect. Also when the average level of NDVI was kept the same, that is, within the same class, the level of variation in the surrounding area did not significantly correlate with the number of species per transect or plot. At a larger scale, however, patterns do emerge. When comparing landsystems, the correlation between the spatial variation and the number of species in 125 sample
34 Productivity – biodiversity patterns in central Australia: Results
plots is not significant, but the scatter plots do strongly suggest a positive trend (fig 11). Vegetation types also show a general increase in species number with increasing spatial variation (fig 12), although the interaction with the mean NDVI disturbs this trend: two sand dune vegetation types have relatively high species number in spite of low spatial variation – these however are close to the ‘optimum’ productivity level. Two river-related vegetation types have low species numbers in spite of high spatial variation in NDVI – two reasons can be the cause of this pattern: as mentioned before, the high cover causes high variability in NDVI values which have limited ecological significance, and also they have productivity levels above the ‘optimum’ for species richness.
5.9 Vegetation types The grouping of transects based on the species composition produced classes that were recognizable as several vegetation types (table 4). These vegetation types differed in their species numbers, which was to be expected, as they were based on species composition, but they also differed significantly in their mean NDVI and temporal variation in NDVI. The relationship between the species numbers and NDVI measures per vegetation type, become apparent in fig. 13, where the order of the vegetation groups is determined by the different NDVI values and compared to the species numbers.
Veg types in mean NDVI classes Veg types in mean NDVI classes 17 180 100% 16 160 15 80% 140 13 120 60% 12 100 11 80 40% 10 60 9 40 20% Numberoftransects 7 20 0 0% 5 MN1 MN2 MN3 MN4 MN5 MN1 MN2 MN3 MN4 MN5 2 a. NDVI class b. NDVI class 1
Figure 12 Distribution of vegetation types over the mean-NDVI classes, as number of transects (a) and percentage of transects (b). It is interesting to notice, that some vegetation types that have quite different characteristics in the field, do actually contain roughly the same species, and are hance classified as one group. This is especially true for group 2, which includes several types of scrubland, and also the spinifex community of Triodia brizioides, which often harbours a lot of the species also present in the surrounding scrublands. Those transect containing Triodia brizioides but few other species, are separated, but in fact this group also contains grasslands with only a low spinifex cover.
Figure 12 gives an impression as to which extent the vegetation types can be distinguished on the satellite images using the mean NDVI. Areas falling in different NDVI classes do have different vegetation types, but there is clearly not a one to one relationship between vegetation types and NDVI classes. The large and variable vegetation group 2 occurs along the whole NDVI range, but other groups are restricted to a subset of the classes. Floodplain vegetation group 9, for instance, is most abundant in the highest mean-NDVI class, decreases to the middle NDVI class,
35 Productivity – biodiversity patterns in central Australia: Results
and is absent in the lowest two classes. River Red Gum dominated plots are also most abundant in NDVI class 5, but surprisingly also appears in lower classes. This is probably caused by the high spatial heterogeneity in riverbeds, where high NDVI rows of trees alternate with bare riverbeds or sparsely vegetated banks or channel bars – any inaccuracy in the GPS coordinates could cause the transect to be projected in a much lower NDVI area. The mulga vegetation occur most in NDVI class 4, which agrees with the impression in the field. The highest NDVI class consists of rivers with rever red gum, floodplain-, mixed- and mulga scrubland. Most communities falling in the lowest NDVI class are classified in the large group of ‘mixed scrubland’. This does not agree with the impression from the field, but is explainable, because these ‘mixed shrubs’ are probably those transects that have high cover of Triodia brizioides and low cover of shrubs. The transects in vegetation group 12 that appear in NDVI class 2 are most likely those that are grassland with some spinifex.
Mean NDVI per veg.type # species per transect, per veg.type 0.05 15 0.00
-0.05 10
-0.10 5 -0.15 species # mean mean NDVI -0.20 0 -0.25 12 15 13 2 17 5 10 11 7 1 16 9 12 15 13 2 17 5 10 11 7 1 16 9 vegetation type vegetation type
Temporal variation per veg. type # species per transect, per veg.type 0.20 15
0.15 10 0.10 5 0.05 species #
temporal temporal sd NDVI 0 0.00 11 10 15 12 7 1 5 13 2 16 17 9 11 10 15 12 7 1 5 13 2 16 17 9
vegetation type vegetation type
3x3 spatial variation per veg. type # species per transect, per veg.type 30 15 25 20 10
15 5
10 species #
5 spatial sd NDVI (resc) NDVI sd spatial 0 0 10 11 15 12 17 1 13 2 5 7 9 16 10 11 15 12 17 1 13 2 5 7 9 16
vegetation type vegetation type
Figure 12 Patterns of species numbers in different vegetation types produced by sorting of mean NDVI (a), temporal variation (b) and spatial variation (‘c). Graphs on the right are sorted in the same order as the graphs to their left.
36 Productivity – biodiversity patterns in central Australia: Results
Table 4 List of vegetation types based on the similarity of species composition. The number of transects that fall within a group, short descriptions and the main occuring species are listed. No of Veg Vegetation type Main or typical species transects nr 67 1 Mulga scrubland Acacia aneura, Acacia kempeana, Acacia estrophiolata, Senna artemisioides subsp. filifolia, Triodia basedowii 179 2 Mixed scrubland Acacia aneura, Acacia kempeana, Acacia tetragonophylla, Senna artemisioides subsp. artemisioides, Senna artemisioides subsp. helmsii, Atalaya hemiglauca, Hakea suberea, Eremophila latrobei, Eremophila freelingii, some transects with high cover of Triodia brizioides 9 5 Sheltered mountain Callitris glaucophylla, Ficus platypoda, vegetation Atalaya hemiglauca, Eremophila freelingii, Pandorea doratoxylon, Dodonea viscosa ssp. mucronata, Capparis michellii, Triodia hubbardii 3 7 MacDonnel-mulga Plectrachne melvillei, Acacia scrubland macdonnelliensis ssp. macdonnelliensis, Eremophila latrobei 20 9 Open floodplain Acacia estrophiolata, Hakea eyreana, scrubland Acacia pruinocarpa, Acacia victoriae 30 10 Triodia basedowii on Triodia basedowii, Grevillea juncifolia, sand dunes Acacia ligulata, Grevillea stenobotrya, Eremophila wilsii 6 11 Triodia pungens on Triodia pungens, Eucalyptus gamophylla, sand dunes Dodonea viscosa subsp. angustissima 16 12 Triodia brizioides on Triodia brizioides, few other species. dolomite/limestone Either spinifex cover or grassland with outcrops some tussocks of spinifex. 18 13 Spinifex and mallees Triodia brizioides, Callitris glaucophylla, on mountains Acacia dictyophleba, Grevillea wickhamii, Eucalyptus eremaea, Eucalyptus gillenii, Eucalyptus intertexta, Eucalyptus sessilis, Prostanthera sericea, Triodia hubbardii 24 15 Open grassland Sparse individuals of e.g . Acacia kempeana, Acacia pruinocarpa, Senna artemisioides subsp. filifolia, Senna artemisioides subsp. artemisioides 20 16 Rivers with river red Corymbia/Eucalyptus camaldulensis, gum Melaleuca bracteata, Melaleuca glomerata, Acacia victoriae 5 17 Rivers in mountains, Melaleuca bracteata, Melaleuca without river red gum glomerata, Atalaya hemiglauca
37 Productivity – biodiversity patterns in central Australia: Discussion
6 Discussion The clear-cut reasoning of 1 + 2 = 3, that is, if biodiversity is related to productivity (1), and productivity can be measured by remote sensing (2), than remote sensing data can be related to biodiversity (3), has not been shown to be true in this research. However, that does not mean that logic does no longer function. More likely, the relationship between biodiversity and productivity, as well as that between remote sensing data and productivity, are either not very strong in the studied area, or they have not been measured in the most effective way.
6.1 Vegetation cover The best correlation with perennial species numbers found in this study is that with the cover of perennials as measured in the field. The best correlation with mean NDVI, however, is that with the total vegetation cover, including herbs and grasses. This does indicate a discrepancy between the measure used for productivity, the mean NDVI, and the factor most important for determining species numbers, cover of shrubs and trees and spinifex. It is interesting to notice too, that productivity from NDVI gives a negative correlation with species numbers, while the correlation of species numbers with cover, also a measure of productivity, is strongly positive. This certainly indicates that the results need to be considered with some reservation. The estimated cover included dry or dead vegetation, while the NDVI only reacts to green vegetation, and can in fact be lowered by surrounding dead vegetation (see below, ‘Influences on NDVI’). Based on the results, it may prove more informative in terms of species richness, to use an index that measures total vegetation cover rather than green cover. Such an index, the PD54 (Pickup et al. 1993), has been developed and applied successfully in central Australia. Special attention should be given to influence of the type of plants surveyed. As only perennial species were studied, the absence of a relationship with temporal variability might not be surprising. A relationship was expected with persistence of productivity, but the variability may not be the best measure of persistence. A better measure might be the minimum level, an idea that will need further exploration. Annual plant species diversity is expected to behave in a different way to the perennials. As opposed to perennials, annual species can partition time as well as space: an area may be covered by different sets of plant species at different times of the year. Because of this, temporal variability is expected to increase species diversity of annual plants. The cover of annual plants is more related to variability in productivity than to the mean level, but whether and how the cover of annuals is related to their diversity is unknown.
6.2 Relationship productivity- species richness Waide et al. (1999) summarize the relationships between diversity and productivity found in 154 articles, covering studies in a wide variety of ecosystems, scales and organisms. Unimodal, positive, negative and no relationships were found, the frequency of their occurrence depending on the spatial scale (either defined by organization scale or area), the type of organism (plants vs. animals, and different groups of vertebrates) and the type of ecosystem. Only some of their spatial scale categories are relevant to this study, being the across community type (excluding the within community and the continental to global scale) and the <20 km (local) and 20- 200 km (landscape) scales (excluding regional and continental to global). Across communities the majority of relationships of plant biodiversity with productivity is unimodal (40%), while almost as many studies have found no relationship (38%), and positive and negative relationships are both found in ca. 10 studies. A similar pattern
38 Productivity – biodiversity patterns in central Australia: Discussion
emerges at the local scale, while at landscape scale over 50% of relationships are not significant, and 20% are unimodal and ca. 15% are positive and as many are negative.
Different research scales usually cover areas of different sizes, but they should also represent different scales of variation. In the present study the comparisons with mean NDVI per plot and per transect within landsystems could be considered equivalent to Waides’ local level, while the whole study area is at the landscape scale. Three of the landsystems appear to harbour a unimodal relationship between mean NDVI and species numbers, while one relationship is a significant positive correlation, one is negative, and four have no significant pattern. At the landscape scale, all transects could be compared, but it would be more meaningful to compare data between landsystems. Although the sampling was not designed to give an unbiased estimate of species numbers per landsystem, that is, the sampling was not done in a random manner, we have used the data to get some idea of possible relationships. Although landsystems differ significantly in their species numbers and mean NDVI values, no clear relationship was found between these two variables. It is possible to call comparing all plots or transects in the study-area working at the landscape scale, as the samples are spread over a whole landscape, but the local variation included in these data makes it a smaller-scale comparison.
Across-community relationships are represented by the comparison of vegetation types. The differences at this level are in fact more pronounced than they are at all other scales tested. Fig 13 gives a strong suggestion of a humpbacked relationship, with the exception of two vegetation types (10 and 17, see table#) with relatively low species numbers. The sand dune vegetations of group 10, as well as those of group 11 are situated in areas with very low spatial heterogeneity, as well as low temporal heterogeneity. This could be expected to decrease species numbers per transect, which in case of group 10 it seems to do. On the other hand, the medium mean NDVI seems to increase the species number compared with other vegetation types with low temporal and spatial variability. The highest NDVI values were found in river red gum dominated rivers, which have very few other plant species. Perennials need to be resistant too heavy flooding in these habitats, a trait not found in many except from the large gumtrees. These rivers cause the extreme values to the right of the main data-cloud in figure 6, and may be responsible for a substantial part of the negative correlation found between mean NDVI and species richness in plots and transects.
One effect of spatial configuration is the relatively high number of perennial species in the inhospitable low productivity areas of annual grasses and herbs. These species numbers would probably be lower if there were no sources of seeds from surrounding higher productive areas – the populations supported by low productive areas would not be able to survive, because their numbers are too low (Tilman & Pacala 1993). Of course this does not apply to the few species especially adapted to the harsh conditions that characterize most low-productive areas, the most common species being spinifex grasses. Most of the species found in open areas and between spinifex on limestone outcrops, the two lowest-NDVI environments, are also found, in larger numbers in more productive areas. This source-principle works at the lowest scale levels of 30-m areas and adjoining vegetation types, and the smallest time-scale of year-to-year survival of individuals. At larger distances, seed dispersal distances may become a limitation to establishment of individuals, especially for the many ant-dispersed Acacias etc. Most plant-populations will depend on variation within rather than between regions or landsystems.
39 Productivity – biodiversity patterns in central Australia: Discussion
A model presented by Gough et al. (1994), shows the double role of environmental conditions. They determine the potential richness, but also influence the biomass, where biomass in turn effects competition, and competition together with the potential richness result in the realized richness. If environmental conditions and biomass are well correlated, estimating biodiversity from biomass could be used as a quick way around having to know the environmental conditions. However, those environmental variables that are not related to productivity, but do influence species numbers, will need to be included in any model of species richness.
Gough et al. (1994) found very weak correlations between aboveground biomass and species richness in marsh communities. Some environmental variables measured (elevation, salinity and soil organic matter) proved to be much better predictors in a regression analysis, but restricting the analysis to a narrower range of environmental conditions improved the correlation between species richness and biomass considerably. This indicates that conditions not directly related to biomass or productivity can disguise relationships between species richness and productivity, or simply overrule them. Therefore it is important in studying this relationship, to consider the range of environmental conditions sampled, the scale at which they operate and their effect on the productivity. When comparing a sandy and a limestone area, for instance, the soil conditions may sustain similar vegetation cover, but very different species and species richness. Within the sandy area, dune-crests and valleys will represent different hydrological conditions, reflected in cover as well as species numbers. Now within the sandy area the relationship may be clear, while the combination with the limestone area may confuse the patterns. Therefore stratification into landscape types or inclusion of auxiliary data in models could greatly enhance predictions by productivity (e.g. Franklin et al. 1994). Including NDVI measures could probably improve predictive models of occurrence of individual species based on other environmental factors included in GIS layers, such as those developed by Griffin (1997b+c, Griffin & Tier 1997, Griffin & Chewings 1997, Tier & Chewings 1997).
The influence of factors not related to productivity is also apparent in our data, where different conditions can produce vegetations that may have the same level of productivity. For instance, species-poor mulga ( Acacia aneura ) scrublands and species-rich mixed scrubland have very similar productivity patterns, so that their difference must be due to other factors, such as soil conditions or grazing pressure. Also, scrubland in rocky mountainous terrain, and spinifex on sand dunes can have the same medium productivity level, but differ strongly in their species composition and -richness. In the latter case, spatial patterns are very different between the two landscape types. These patterns, if exploited more than has been done in this study, could be important inputs for either stratification of the landscape or direct inclusion in models.
6.3 Species The biodiversity was defined by the number of perennial plant species. However, many species in the arid zone are very variable taxonomically, most notably mulga (Acacia aneura ) (Pedley 1973). This species can grow as a shrub or as a tree, the phyllode (stem structure with leaf functionality) shape ranges from filiform to elliptic, the size from 1 to 25 cm in length and the colour can be more green, grey or silvery- blue. The pods are also variable, and all these differences can occur between individuals in the same stand (Fox 1986). On the other hand, mulga is sometimes difficult to distinguish from other Acacia-species. It could be questioned whether species are the best ‘units’ to define diversity in such groups of species, especially since variability within some species may be greater than morphological or ecological differences between species.
40 Productivity – biodiversity patterns in central Australia: Discussion
Apart from that, the restriction to perennial plants was also a restriction to inferences about biodiversity.
6.4 Habitats for other taxa Only plant biodiversity was surveyed during this study, for practical reasons more than anything. Bird surveys were contemplated, but the great temporal variation in bird occurrence would have caused too much variation in monotemporal field data (Jørgensen & Nøhr 1996). However, plants are only at the base of the biodiversity pyramid. Bird distribution for one thing, is known to be related to vegetation characteristics, including structure, species composition and spatial heterogeneity (e.g. Recher 1969, Böhning-Gaese 1997, McCollin 1993, Freemark & Merriam 1986), and the same is true for other organisms (e.g. Abensperg-Traun et al. 1996). The relationship of animal diversity to productivity is expected to differ from the patterns of perennial plants found in this study. An example in the studied environment is the vegetation in rivers, which is usually dominated by large gum trees (river red gum, Eucalyptus camaldulensis ) and very few other plant species. Although the ‘biodiversity’ is low when looking at perennial plants only, it is in fact very high, as the trees, which often are partly hollow, provide a habitat for many other species, including many birds, insects and reptiles. If such patterns are known, remote sensing in combination with models of habitat requirements for specific species or groups of species can provide useful information about the distribution of possible habitats or high-biodiversity areas. This can help for instance to assign protected areas for species, such as the most famous threatened species, the Giant Panda (De Wulf et al. 1988), or the central Australian rufus hare-wallaby (Lagorchestes hirsutus )(Saxon 1983). Information from RS spectral data could also provide direct clues to the physical ground conditions of species habitats. In a study on the distribution of the wader Dunlin ( Calidris alpina ), Landsat MSS images (band 7) were used to estimate soil wetness of moorland, which indicates the habitat suitability for this wader, and was well correlated with the amount of dunlin observed (Avery & Haines-Young 1990). Productivity and the persistence of productivity could be important parameters for an area’s potential to sustain animal populations, especially in an arid or semi-arid environment, where animal populations need to survive through dry periods.
6.5 Data characteristics
6.5.1 Influences on NDVI The correlations between NDVI measures and vegetation cover are quite high, especially considering the crudeness of the field estimates of cover. In a previous study in central Australia, Foran & Pearce (1990) fitted a simple linear model with r2=0.96 through NDVI and total green cover from more elaborate measurements. The higher variation in the present study could be due to the field estimates and spatial inaccuracies but also to non-vegetation NDVI influences, such as soil background and dry plant material (Clevers 1997, Huete & Tucker 1991, Huete 1988, Huete & Jackson 1987, Elvidge & Lyon 1985). Using a vegetation index reduces the influence from differences in soil background considerably, but some influence remains. The influence from soil brightness has been found to be most disturbing in areas with intermediate vegetation-cover, where scattering of light between vegetation and soil causes complex interactions that are difficult to correct for (Huete 1988). As a result of this, ratio-based indices overestimate the vegetation on dark backgrounds and underestimate it on light- coloured background, while orthogonal-based indices behave the opposite way (Elvidge & Lyon 1985). Some correction for soil background is possible by adjusting
41 Productivity – biodiversity patterns in central Australia: Discussion
for the slope and origin of the soil-line, which has resulted in several extensions of existing vegetation indices (Huete 1988, Paltridge & Barber 1988, Richardson & Everitt 1992, Clevers 1997). For instance, the Soil Adjusted Vegetation Index (SAVI) is the NDVI with the origin shifted by L (=l 1, l 2): ((NIR+l 1)-(Red+l 2))/(NIR+l 1)+(Red+l 2)). The optimal choice of L depends on the vegetation cover, but a value of 0.5 (when using reflectance units) was found to improve the index compared to the NDVI and the perpendicular vegetation index (PVI) (Huete 1988). Another aspect of soils, apart from their brightness, is their colour, which affects vegetation indices most if vegetation cover is scarce. Red soils have been found to increase greenness values (Huete & Jackson 1987). This effect is not corrected by the SAVI (Huete 1988). In (semi-) arid regions vegetation cover usually ranges from very low to intermediate, but full cover also occurs in places or at times. Fortunately the soil is dry nearly all the time, so that temporal comparison of vegetation indices is not disturbed by differences in soil moisture. Spatial comparison, however, may be influenced by soil differences. In the studied region soils were mostly red sand and loam, but white to yellow river sand, grey dolomite, orange soil from gneiss rocks and red-brown (layer on some rocks, or some mixed in organic matter) substrates also occur. Very dark soils were not encountered though, and differences are probably more related to colour than to brightness, although these are related. Two other non-vegetation factors can influence the NDVI. Atmospheric water vapour has more influence on NIR than on red light, decreasing NDVI values (Huete & Tucker 1991). In this study atmospheric conditions were somewhat corrected for by dark pixel subtraction, but some influence may remain. Dead plant material is another source of disturbance, lowering the signal from the green material by shading and by scattering reflected light (Huete & Jackson 1987). This is especially important in perennial grasses if the young green leaves are hidden underneath old dry leaves, which is particularly true for spinifex. The very low NDVI values for spinifex vegetations on dolomite may be caused by the combination of a relatively light substrate in combination with high percentage of dead plant material. According to Huete & Tucker (1991) the error in NDVI can be around 0.1 for a 50% vegetation cover, due to soil and atmospheric influences. Whether this is a serious problem will depend on the area studied and the objective of the study. In this case the patterns observed and the results from Foran & Pearce (1990) do not indicate a problem and in spite of all the possible error, the NDVI is commonly used for estimation of green vegetation cover worldwide, and also in arid and semi-arid environments (e.g. Foran & Pearce 1990, Prince 1991, Tucker et al. 1991a, Tucker et al. 1991b, Lobo et al. 1997). The NDVI is also used in a similar study, related to the present one, using NOAA-AVHRR 10-day composite NDVI data for all of inland Australia (Vanessa Chewings pers.comm. ).
6.5.2 Spatial accuracy A certain part, maybe substantial part of the unexplained variation may be due to spatial inaccuracy in the GPS data and misregistration of the images. The fact that relationships of species numbers with cover measures from the field, being simultaneous observations not prone to spatial inaccuracies, are stronger than those with productivity measures from RS, influenced by two added inaccuracies, adds to this suspicion. The accuracy of autonomous GPS has increased greatly since the USA military have removed the scramble signal in the beginning of 2000. Therefore the second set of field data had 10 to 20 meter accuracy, which is as much as is needed, especially since the image accuracy is also +/- 30 meter. The dataset from 1995 has much poorer spatial accuracy, with errors up to 100 m. Although even with 20 m errors plots can be placed in the wrong pixel, this neighbouring pixel is more likely to be similar to the ‘correct’ pixel than a pixel further away, because of the spatial
42 Productivity – biodiversity patterns in central Australia: Discussion
coherence of the data. If this inaccuracy has an important influence on the outcome of the analyses, the two datasets should show considerable differences in the observed patterns, and resampling to 100x100 m pixels should also improve the outcome. However, patterns were consistent between the surveys, and results from analysis with resampled data did not differ much from those done with the fine-scale data.
6.5.3 Behaviour of variation Temporal and spatial variability are correlated – this will be at least in part due to misregistration of the multitemporal images: the images of the different dates will not fit over each other exactly. Consequently the pixels may represent slightly different areas, and the variation of the ‘same’ pixel in time will increase if spatial variation is high. Variation in NDVI is also correlated to the mean NDVI (plots as sample: r = 0.398, p<0.001), but this is mainly caused by some extreme values of very high mean NDVI and variation. The coefficient of variation was less strongly correlated with the mean (r = 0.121, p<0.001), but the relation to species numbers was not improved by using this measure instead of the standard deviation. In fact the correlation coefficient of temporal variation with species numbers, which was insignificant anyway, got even closer to zero when using the coefficient of variation.
As the NDVI is calculated as a ratio, a raise in stdev with higher means is not a mathematical rule. It is related to the vegetation dynamics; areas with very low mean NDVI are those with low cover alternating with no cover, or those with spinifex vegetations, which have reasonable cover but very low green reflection – both of these can’t possibly have very high variance, because all values are low. Areas with very high mean NDVI are mostly rivers and the river plains of Todd River. These have reasonable high cover most of the time thanks to the River Red Gums, but in wet times the cover is extremely high because of very lush growth of grass and herbs. In NDVI and productivity terms, the difference between green and very green is greater than the difference between bare and almost bare. However, the ecological implications of the variation are probably not proportional. Excluding from the 2022 plots those 38 with the highest NDVI values, decreased the correlation coefficient (r) between mean and stdev NDVI from 0.40 to 0.26, and excluding 110 plots reduced it further to 0.11 (still p<0.001). A similar reduction was true for the correlation mean NDVI with spatial variation, but the correlation between spatial and temporal variation stayed around r=0.4.
6.5.4 Measuring spatial variation The measure of spatial variability used in this study is among the very basic ones, although it has been successfully used in similar contexts before (Coops et al. 1998). Many more elaborate methods and indices have been developed, some of which may give better information about the spatial heterogeneity relevant for species co- existence. Several measures of spatial variation have been used in studies of vegetation type or cover with remote sensing. Jørgensen & Nøhr (1996) used two diversity indices (Shannon & Weaver 1949, Simpson 1949) on the number of pixels or the number of patches in different classes. Chen & Brutsaert (1998) used semivariograms to describe spatial patterns in NDVI measurements of a prairie vegetation. A semivariogram or variogram is a graph of semivariance (y-axis) between points at increasing distance (‘lags’, x-axis) from each other, and can describe the nature and extent of spatial dependence (Atkinson 1999, Isaaks & Srivastava 1989). The advantage of this method over the one used in this study is, that the variance is
43 Productivity – biodiversity patterns in central Australia: Discussion
calculated over distances rather than over areas, and whereas the areas are nested, the variances at larger distances are independent of those at smaller distances. Also, the shape of the variogram gives more information about spatial patterns than do variances in areas. Variograms are usually calculated for some polygon, to describe the spatial pattern of an area (Chen & Brutsaert 1997, Cohen et al. 1990). They can, however, also be used to calculate local spatial attributes. Image classification has been found to improve by using the semivariance at one or more lags for each pixel as a measure of spatial pattern (Berberoglu et al. 1999, Lloyd et al. 1999; in Curran 1999, Miranda et al. 1997, 1992). In at least one study, this measure was more informative than the standard deviation (Berberoglu et al. 1999, Lloyd et al. 1999; in Curran 1999). De Jong & Burrough (1995) described a method for generating a spatial distributed measure of pattern, which they call a fractal dimension. They explore two methods for estimating the fractal dimension from satellite data, one of which is based on the slope of the log-transformed variogram, calculated around and assigned to individual pixels.
Fractal patterns are self-similar across scales. Fractal dimensions can be used to compare the complexity of spatial patterns between areas, but also to study variance within a system between scales. If systems show large changes in fractal dimension at a certain scale-level, this indicates that there may be a change in processes influencing the observed patterns (Horne 1995). This use of the fractal dimension already indicates, that patterns are often only self-similar across a limited number of scales (Clarke 1986, Ritchie & Olff 1999), if they are at any. As they are not necessarily self similar for the scales studied, the pattern studied may not be truly fractal, so that fractal methods may not produce satisfactory results (De Jong & Burrough 1995). Fractal dimensions have recently had some attention in ecology (Bradbury et al. 1984, Milne et al. 1992; in Tyre et al. 1997, Ritchie & Olff 1999) for describing habitat characteristics related to biodiversity. Combining this ecological theory and new RS techniques, could yield some interesting results.
6.5.5 Scale The importance of scale in ecological research has been recognized long ago, but has received a lot of attention lately (e.g. Tyre et al. 1997, Horne 1995, Böhning- Gaese 1997, Atkinson 1997, Garret & Dixon 1997, Levin 1992). It is also a very important consideration for remote sensing applications (e.g. Townshend & Justice 1988, Curran & Atkinson, Stein et al. 1998).
Linking remotely sensed data to ecological field-observations should be done with consideration of the support-size of the measurements. If variability within a large pixel is high, the ground-supports will need to be either the size of the pixel, or a representative sample within the pixel. If the measured variable is spatially dependent (that is, if samples closer together are more likely to be similar), geostatistical methods such as kriging could then be used to calculate a natural mean (Atkinson 1997). If they are spatially independent, an ordinary mean (or summation, depending on the property measured) will be more meaningfull.
The outcome of any type of spatial variation measure for RS images will be dependent on the pixel size of the image (Curran & Atkinson 1999). Not only the smallest spatial scale of variation that can be observed depends on the pixel size, but also the support of the pixel-values, and with that the variation between pixels. Sèze & Rossow (1991) compare the effect of resampling satellite data to a coarser spatial resolution by averaging, or by sampling a number of the high-resolution pixels.
44 Productivity – biodiversity patterns in central Australia: Discussion
Taking a sample preserves the histogram statistics, while averaging the data changes these statistics and other relationships in the data, such as radiance correlations. Using a sample of high-resolution pixels has the advantage of reducing the file-size, but expensive high-resolution images still need to be purchased, while averaging can be replaced by using coarser-resolution images. However, the scale of the studied variation should be carefully considered before going for the cheaper option.
In this study, small-scale variability was the factor best related to species richness at the regional scale (between landsystems). Using 1 km NOAA-AVHRR images instead of Landsat TM or MSS, would probably take away this variation. This means that these data are probably not suitable for comparing landsystems. They may however be very useful for comparing regions, where the variation between landsystems may be more important than that within.
7 Conclusion & recommendations Patterns of species richness in relation to productivity and temporal and spatial variation in productivity vary across spatial and organizational scales and between different landscape types. The NDVI measures used can only explain a small part of the variation, but their interaction may hold more information than was extracted from it in this study. The relationship between biodiversity and productivity, as well as that between remote sensing data and productivity, were not simple in this study. Several factors can be responsible for disguising potential patterns, e.g. the measure for biodiversity, the spatial accuracy of the data, non-vegetation influences on the NDVI, the scale of variation, and the measures for productivity and persistence of productivity. Some alternative methods have potential for improving the results. Within the short time span of this project, these could not be tested, but they may be of interest to others. Including animal species and annual plants is expected to produce different patterns. Field sampling for these groups will take several surveys through time, because of their spatial and temporal variability respectively, but could be quite rewarding. Most improvement is probably possible in the remote sensing methodology. One idea is to use a different measure of temporal variation, for instance the range of NDVI values (maximum – minimum), or to just use the minimum (related to persistence) and maximum (related to potential for productivity). Relationships may also be stronger if a measure for total vegetation cover is used, e.g. the PD54 (Pickup et al. 1993). This could at least be an alternative for a mean productivity measure, while minimum NDVI may still be the best persistence measure. It will also be interesting to compare these data to other spatial resolution data. This was not done in this study because of difficulties in acquiring suitable data of different resolutions at the same dates – this was considered necessary to avoid including temporal variation in the comparison. Coarser resolution images would not include small-scale patterns, but if species richness were better related to large-scale patterns, this would not be a great loss. If NOAA AVHRR data would produce satisfactory result, using these data would considerably decrease the costs, storage requirements and processing time. Extending the use of the spatial domain, by one of the methods described above, is also expected to increase the amount of useful information that can be derived from remote sensing. Combining the RS data with existing environmental data such as a DEM or geological map, could also improve predictions considerably.
45 Productivity – biodiversity patterns in central Australia: References
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50 Productivity – biodiversity patterns in central Australia: Appendix 1
9 Appendix 1 Table 4 Files used for geometric rectification of raw Landsat TM data, using pre-existing files, created by Graham Pearce, containing ground control points (GCPs) in image pixel positions, and the corresponding UTM coordinates (zone 53, south). These files were combined in excel to form GCP files in Envi format. The registration was only applied to a subset of the image, although all available GCPs were used. The subset includes 3 bands, TM 2, 3 and 4 (corresponding to band 1, 2 and 3 in the new image), and the area used has the top-right corner at UTM 331100.000 E, 7403700.000 N, and the size is 65300.00 m E-W and 82500.00 m N-S. The warp method used was second degree polynomial, with nearest neighbour resampling and background value 0.000 for all images. 9.1.1.1.1.1 I Raw image GCPs UTMs Envi GCPs Output file m file (.img) (.gcp) (.gcp) (.pts) (.img) a g e d at e 14-02-1988 T10277A Feb88 TMMAP-77 TM288 TM288r 05-06-1988 T10277B Jun88 TMMAP-77 TM688 TM688r 03-02-1990 T10277C Feb90 TMMAP-77 TM290 TM290r 09-02-1992 T10277D Feb92 TMMAP-77 TM292 TM292r 13-12-1994 T10277E Dec94 TMMAP-77 TM1294 TM1294r 05-03-1995 T10277F Mar95 Mapmar95 TM395 TM395r 01-12-1995 T10277G Dec95 Map1295 TM1295 TM1295r 18-11-1996 T10277H Nov96-77 TMMAP-77 TM1196 TM1196r 10-03-1997 T10277I 10mar-77 TMMAP-77 TM397 TM397r
Table 5 DN values subtracted in the dark pixel atmospheric correction procedure of the Landsat TM images. 9.1.1.1.1.2 Image Input file TM 2 TM 3 TM 4 Output file date 14-02-1988 TM288r 28 38 36 TM288r-du 05-06-1988 TM688r 12 9 6 TM688r-du 03-02-1990 TM290r 20 23 20 TM290r-du 09-02-1992 TM292r 28 37 30 TM292r-du 13-12-1994 TM1294r 28 43 35 TM1294r-du 05-03-1995 TM395r 20 22 22 TM395r-du 01-12-1995 TM1295r 27 18 0 TM1295r-du 18-11-1996 TM1196r 32 42 40 TM1196r-du 10-03-1997 TM397r 25 26 30 TM397r-du
Table 6 NDVI-files and band-number of NDVI from different years in the compiled NDVI file, NDVItmnz.img. The NDVI was calculated by function bm_ndvi (see box 1), using TM band 3 (Red) and 4 (NIR). 9.1.1.1.1.3 Image Input file 9.1.1.1.1.4 Output NDVItmnz- date file band 14-02-1988 TM288r-du NDVItm288nz 1: Feb-88 05-06-1988 TM688r-du NDVItm688nz 2: June-88 03-02-1990 TM290r-du NDVItm290nz 3: Feb-90
51 Productivity – biodiversity patterns in central Australia: Appendix 1
09-02-1992 TM292r-du NDVItm292nz 4: Feb-92 13-12-1994 TM1294r- NDVItm1294nz 5: Dec-94 du 05-03-1995 TM395r-du NDVItm395nz* 6: March-95 01-12-1995 TM1295r- NDVItm1295nz 7: Dec-95 du 18-11-1996 TM1196r- NDVItm1196nz 8: Nov-96 du 10-03-1997 TM397r-du NDVItm397nz 9: March-97 * subsetted in bandmath to match image size of other images.
; function calculates NDVI for an image while ; replacing 0 values in either band by 1. function bm_ndvi, b1, b2 band1 = float(b1) zero3 = where(band1 eq 0.0, count) if count ne 0 then band1[zero3] = 1.0 band2 = float(b2) zero4 = where(band2 eq 0.0, count) if count ne 0 then band2[zero4] = 1.0 result = (band1 - band2) / (band1 + band2) return, result end Box 1 IDL function used for calculating NDVI: bm_ndvi. B1 = TM band 4 (NIR), b2 = TM band 3 (Red).
Table 7 Mean and standard deviation of NDVI values per layer (=image, = date). NDVItmnz- Mean NDVI Stdev NDVI band 1: Feb-88 -0.034 0.065 2: June-88 0.162 0.087 3: Feb-90 0.017 0.048 4: Feb-92 0.001 0.058 5: Dec-94 -0.108 0.069 6: March-95 -0.027 0.119 7: Dec-95 -0.069 0.083 8: Nov-96 -0.070 0.054 9: March-97 0.079 0.128
Table 8 Functions used for calculating mean and variance and standard deviation of the NDVI over time. b1...b9 are the NDVI images of different dates, b10 is the mean of these dates. The image of March 1995 has a cloud on it, and the area of the cloud is excluded from the calculations by using a mask. In the mask cloudmask the cloud has value 0 and the rest of the image has value 1, while in cloudmaskon it is the reverse. Only the cloud-area has the 8-band mean and variance (excluding the March 95 band), while the rest of the image has the total 9-band mean and variance. Band math function Calculates Variables defined Output file (b1+b2+b3+b4+b5+b6+b7+b8+b9)/ Mean of 9 b1...b9 = NDVI images NDVImean9 9 bands of different dates
52 Productivity – biodiversity patterns in central Australia: Appendix 1
((b1-b10)^2+(b2-b10)^2+(b3- Variance of b1...b9 = NDVI images NDVIvar9 b10)^2+(b4-b10)^2+(b5- 9 bands of different dates, b10 b10)^2+(b6-b10)^2+(b7- = NDVImean9 b10)^2+(b8-b10)^2+(b9-b10)^2)/9 (b1+b2+b3+b4+b5+b7+b8+b9)/8 Mean of 8 b1...b9 = NDVI images NDVImean8 bands of different dates ((b1-b10)^2+(b2-b10)^2+(b3- Variance of b1...b9 = NDVI images NDVIvar8 b10)^2+(b4-b10)^2+(b5- 8 bands of different dates, b10 b10)^2+(b7-b10)^2+(b8- = NDVImean8 b10)^2+(b9-b10)^2)/8 (NDVImean9 * cloudmask) + Excludes NDVImeanco (NDVImean8 * cloudmaskon) cloud-NDVI mp (NDVIvar9 * cloudmask) + Excludes NDVIvarcomp (NDVIvar8 * cloudmaskon) cloud-NDVI (b1)^0.5 Standard b1 = variance-images NDVIstdev… deviation
Table 9 Landsat TM composed variable files and their basic statistics. File Represents min max mean stdev NDVImeancomp Mean NDVI of 9 (8 -0.724902 0.913287 -0.005270 0.054154 under cloud) dates NDVIstdevcomp Standard deviation of 0.000000 0.580234 0.094369 0.036537 NDVI of 9 (8 under cloud) dates NDVIstdev_of_me Standard deviation of 0.000000 0.396825 0.017846 0.013495 an_3x3filter mean NDVI in 3x3 pixel window NDVIstdev_of_me Standard deviation of 0.000000 0.281817 0.034480 0.020496 an_33x33filter mean NDVI in 33x33 pixel window
Table 10 Statistics of the 5 mean-NDVI classes. Class Min Max Mean Stdev Area (km2) Pixels M1 -0.72490 -0.09000 -0.11798 0.04101 116.93 129919 M2 -0.08999 -0.04000 -0.05843 0.01308 1,086.40 1207112 M3 -0.03999 0.00000 -0.01952 0.01124 2,079.49 2310546 M4 0.00001 0.07000 0.02463 0.01831 1,733.29 1925880 M5 0.07001 0.91329 0.12615 0.06651 370.92 412128
Table 11 Statistics of the 4 StDev-NDVI classes.
Class Min Max Mean Stdev Area (km2) Pixels Sd1 0 0.06 0.05339 0.005418 603.26 670285 Sd2 0.06001 0.09 0.074699 0.008383 2382.33 2647037 Sd3 0.09001 0.15 0.112033 0.016043 2008.29 2231434
53 Productivity – biodiversity patterns in central Australia: Appendix 1
Sd4 0.15001 0.580234 0.186355 0.039253 392.74 436379
Table 12 Statistics of spatial variation-filtered images (standard deviation) of different window- sizes (in pixels). Window Min Max Mean Stdev 3x3 0.00000 0.396825 0.017846 0.013495 7x7 0.00000 0.439280 0.025330 0.017041 17x17 0.00000 0.359786 0.031206 0.019310 33x33 0.00000 0.281817 0.034480 0.020496
Table 13 Resampling of 30-m TM images to 100-m and 1000-m resolution. Resampling TM band 3 and 4 using resize function, x- and y-factor 0.3 for 100-m pixels, and 0.03 for 1000-m pixels, resampling by cubic convolution. NDVI calculated using function bm_ndvi (see box 1). 30 m image 100 m image 100 m NDVI Band in TM100NDVI TM288r-du TM288resamp3 TM100_288NDVI 1: Feb-88 TM688r-du TM688resamp3 TM100_688NDVI 2: June-88 TM290r-du TM290resamp3 TM100_290NDVI 3: Feb-90 TM292r-du TM292resamp3 TM100_292NDVI 4: Feb-92 TM1294r-du TM1294resamp3 TM100_1294NDVI 5: Dec-94 TM395r-du TM395r esamp3 TM100_395NDVI 6: March-95 TM1295r-du TM1295resamp3 TM100_1295NDVI 7: Dec-95 TM1196r-du TM1196resamp3 TM100_1196NDVI 8: Nov-96 TM397r-du TM397resamp3 TM100_397NDVI 9: March-97
Table 14 Statistics of mean and stdev NDVI images at different resolutions. Measure Resolution Min Max Mean Stdev Mean 30 m -0.7249 0.9133 -0.0053 0.0542 Mean 100 m -0.6219 0.9009 -0.0052 0.0547 Stdev 30 m 0.0000 0.5802 0.0944 0.0365 Stdev 100 m 0.0000 0.2710 0.0106 0.0102
54 Productivity – biodiversity patterns in central Australia: Appendix 2
10 Appendix 2 Some studies of RS and species diversity Jørgensen & Nøhr (1996) studied bird species diversity in relation to landscape diversity and biomass production in the Sahel at different scales. As a measure for landscape diversity, they used two diversity indices (Shannon & Weaver 1949, Simpson 1949) that used the number of pixels or the number of patches in classes from an unsupervised classification of Landsat TM images. Biomass production was calculated from the Integrated NDVI (INDVI), the combination of NDVI from multi temporal NOAA-AVHRR images. Changes of scale influenced the strength of the correlations, but did not change the general pattern. Bird surveys of different dates showed very different correlations (but always positive) with INDVI and landscape diversity. Fjelså et al (1997) related ecoclimatic stability of African ecosystems, derived from NOAA-AVHRR images, to the occurrence of biodiversity ‘hotspots: areas with high concentration of relict species and neo-endemics. The measure they used was the coefficient of variation of the NDVI and the ratio between NDVI and brightness surface temperature (Ts) of monthly composites. In a continent-wide comparison, they found differences in spatial patterns of climatic stability between areas dominated by evolutionary older or younger species groups, and that mountain forests classified as hotspots were generally more stable than mountain forests lacking high endemism. On a regional scale, however, no clear correlations were found, which is in part attributed to the coarse resolution: yearly NDVI variation was averaged over 25x25-m areas to account for inaccuracy of field-data locations. At a much finer scale, Coops et al (1998) used high-resolution aerial video data to predict habitat quality from spatial variation in the reflectance of Australian Eucalypt forests. They computed the standard deviation (stdev) of different bands in windows of different sizes, and used the maximum mean stdev of a site to compare to field assessments of habitat quality for ground dwelling fauna. As expected, the habitat quality and the stdev of the near infrared (NIR) band were positively correlated. A study on the distribution of the wader Dunlin (Calidris alpina) used Landsat MSS images to estimate soil wetness (Furness et al 1993). Although this study was not focussed on species diversity, it does give a good example of relating RS information on physical ground conditions to the occurrence of species.
In all of the studies above, the spectral domain is used directly through knowledge of the reflection characteristics of objects, e.g. the vegetation, soil moisture or canopy shadows. In many cases information is also derived from temporal and spatial patterns. In some cases spatial patterns are in fact the only information used directly. Mack et al (1997) used existing land-cover maps derived from Landsat TM images, and compared bird species numbers in woodland patches with the size of the patches according to the land cover map and as determined from topographical maps and by field surveys. They found that the land cover map underestimated the size of the patches, and hence did not predict the number of species accurately.
Other studies have used the spectral RS data to classify their images by one of the traditional methods, and have compared the biodiversity between the distinguished classes. Nagendra & Gadgil (1999a) found that classes from supervised classification could distinguish landscape element types of the Western Ghats hills in India, and contained different species and had different species richness, but those from unsupervised classification did not. In a study of meadows in the Greater Yellowstone Ecosystem (USA), unsupervised classification was used in combination with manual merging of classes based on field knowledge of the vegetation. Here it
55 Productivity – biodiversity patterns in central Australia: Appendix 2
was found that species distribution and species diversity did differ between classes (Debinski, Kindscher & Jakubauskas 1999). A disadvantage of using general classification methods is, that no logical connection is established between the RS signal and the ground data. As Tuomisto (1998) put it: “The colour pattern in satellite imagery enable one to identify and map areas that differ in some way; field studies are then needed to find out wether these differences are significant in ecological and floristic terms”.
56 Productivity – biodiversity patterns in central Australia: Appendix 3
11 Appendix 3. Species list.
Code Scientific name Family English name
PTPA Ptilotus parvifolius (F. Muell.) F. Muell. Amaranthaceae PADO Pandorea doratoxylon (J. Black) J. Black Bignoniaceae Spearbush CAAR Senna artemisioides (DC.) Randell ssp. artemisioides Caesalpinaceae Silver cassia Senna artemisioides (DC.) Randell ssp. helmsii (Symon) CAHE Randell Caesalpinaceae Blunt-leaf / Helm's cassia Senna artemisioides (DC.) Randell ssp. oligophylla (F. CAOL Muell.) Randell Caesalpinaceae Oval leaf cassia Senna artemisioides (DC.) Randell ssp. sturtii (R. Br.) CADE Randell Caesalpinaceae Grey cassia Senna artemisioides (DC.) Randell ssp. sturtii (R. Br.) CAST Randell Caesalpinaceae sticky senna PELA Petalostylis cassioides (F. Muell.) Symon Caesalpinaceae Butterfly bush CANE Senna artemisioides (DC.) Randell ssp. filifolia Randell Caesalpinaceae Punty bush / Desert cassia CASP Caesalpinaceae sp1 Caesalpinaceae CAPE Senna artemisioides (DC.) Randell ssp. petiolaris Randell Caesalpinaceae CAPL Senna pleurocarpa (DC.) Caesalpinaceae Firebush / Chocolate senna CAMI Capparis mitchellii Lindley Capparaceae Wild orange ALDE Allocasuarina decaisneana Casuarinaceae Desert Oak RHSN Rhagodia eremaea Paul Wilson Chenopodaceae ATNU Atriplex nummularia Lindley ssp. nummularia Chenopodaceae Old man saltbush RHPA Rhagodia parabolica R. Br. Chenopodaceae SPTE Spartothamnella teucriiflora Chloanthaceae HEKE Helichrysum kempei F. Muell. Compositae CACO Callitris glaucophylla J. Thompson & L. Johnson Cupressaceae Native pine SCSN Scaevola spinescens R. Br. Goodeniaceae Spiny fanflower TRCL Triodia brizioides Gramineae TRPU Triodia pungens Gramineae TRBA Triodia basedowii E. Pritzel Gramineae TRLG Triodia longiceps J. Black Gramineae TRHU Triodia hubbardii N. Burb. Gramineae Spinifex x PRST Prostanthera striatiflora F. Muell. Lamiaceae Striped mintbush PRBA Prostanthera sericea (J. Black) Conn Lamiaceae GOST Gossypium sturtianum Malvaceae Sturt's desert rose ACTE Acacia tetragonophylla F. Muell. Mimosaceae Dead finish ACES Acacia estrophiolata F. Muell. Mimosaceae Ironwood Waxy / Feather veined ACDI Acacia dictyophleba F. Muell. Mimosaceae wattle ACAN Acacia aneura F. Muell. ex Benth. var. aneura Mimosaceae Mulga ACKE Acacia kempeana F. Muell. Mimosaceae Witchetty bush Mimosa bush / Prickly ACFA Acacia farnesiana (L.) Willd. Mimosaceae moses ACLI Acacia ligulata Cunn. ex Benth. Mimosaceae Umbrellabush ACVI Acacia victoriae Benth. ssp. arida Pedley Mimosaceae Acacia bush / Victoria wattle ACPR Acacia pruinocarpa Tindale Mimosaceae Black wattle / Black gidgee ACMA Acacia maitlandii F. Muell. Mimosaceae Maitland's wattle Colony wattle / Murray ACMU Acacia murrayana F. Muell. ex Benth. Mimosaceae wattle
57 Productivity – biodiversity patterns in central Australia: Appendix 3
Code Scientific name Family English name ACBA Acacia basedowii Maiden Mimosaceae Acacia macdonnelliensis Maconochie ssp. ACMC macdonnelliensis Mimosaceae Macdonnell mulga FIPL Ficus platypoda (Miq.) Miq. var. minor Benth. Moraceae Native fig ERGI Eremophila gilesii F. Muell. var. argentea Ewart Myoporaceae Giles desert fuchsia ERLA Eremophila latrobei F. Muell. var . latrobei Myoporaceae ERDU Eremophila duttonii F. Muell. Myoporaceae ERGL Eremophila glabra Myoporaceae Black fuchsia ERLO Eremophila longifolia (R. Br.) F. Muell. Myoporaceae Weeping emubush Sandhill native fuchsia / ERWI Eremophila wilsii Myoporaceae Will's desert fuchsia ERST Eremophila sturtii R. Br. Myoporaceae Turpentine bush ERFR Eremophila freelingii F. Muell. Myoporaceae Rock fuchsia bush ERCH Eremophila christophori F. Muell. Myoporaceae ERGO Eremophila goodwinii Myoporaceae Purple fuchsia bush EREL Eremophila elderi F. Muell. Myoporaceae Desert fuchsia Desert boobialla / Native MYMO Myoporum acuminatum R. Br. Myoporaceae myrtle / Western boobialla ERMA Eremophila maculata (Ker.) F. Muell. var. maculata Myoporaceae Spotted fuchsia EUOP Eucalyptus opaca D. Carr & S. Carr Myrtaceae Bloodwood Melaleuca linariifolia Smith var. trichostachya (Lindley) MELI Benth. Myrtaceae Teatree EUCA Eucalyptus camaldulensis Dehnh. Myrtaceae River red gum EUPA Eucalyptus papuana F. Muell. Myrtaceae Ghostgum MEBR Melaleuca bracteata F. Muell. Myrtaceae Black teatree MEGL Melaleuca glomerata F. Muell. Myrtaceae Inland teatree EUGA Eucalyptus gamophylla F. Muell. Myrtaceae Blue mallee EUSO Eucalyptus socialis F. Muell. ex Miq. Myrtaceae Red mallee EUER Eucalyptus eremaea D. Carr & S. Carr Myrtaceae EUGI Eucalyptus gillenii Ewart Myrtaceae EUIN Eucalyptus intertexta R. Baker Myrtaceae Gum coolibah EUSE Eucalyptus sessilis (Maiden) Blakely Myrtaceae Finke River Mallee EUTR Eucalyptus trivalvis Blakely Myrtaceae JACA Jasminum calcareum F. Muell. Oleaceae Poison creeper HALE Hakea leucoptera R. Br. Proteaceae Needlewood HAEY Hakea eyreana (S. Moore) McGillivray Proteaceae Forkleaved corkwood HASU Hakea suberea S. Moore Proteaceae Longleaved corkwood GRST Grevillea striata R. Br. Proteaceae Beefwood GRJU Grevillea juncifolia Hook. Proteaceae Desert grevillea GRST Grevillea striata R. Br. Proteaceae Rattlepod grevillea GRWI Grevillea wickhamii Meissner Proteaceae Holly grevillea VEVI Ventilago viminalis Hook. Rhamnaceae Supplejack CALI Canthium lineare E. Pritzel Rubiaceae CALT Canthium latifolium F. Muell. ex Benth. Rubiaceae Native currant SALA Santalum lanceolatum R. Br. var. lanceolatum Santalaceae Plumbush EXSP Exocarpos sparteus R. Br. Santalaceae SAAU Sarcostemma viminale (L.) R. Br. Santalaceae Quandong Alectryon oleifolius (Desf.) S. Reyn. ssp. elongatus S. HEOL Reyn. Sapindaceae Rosewood / Bullockbush ATHE Atalaya hemiglauca (F. Muell.) F. Muell. ex Benth. Sapindaceae Whitewood DOAN Dodonea viscosa subsp. angustissima Sapindaceae Sticky hopbush
58 Productivity – biodiversity patterns in central Australia: Appendix 3
Code Scientific name Family English name DOLN Dodonaea lanceolata F. Muell. Sapindaceae Hopbush DOVI Dodonaea viscosa Jacq. ssp. mucronata J. West Sapindaceae Sticky hopbush PIPH Pittosporum phylliraeoides DC. var. microcarpa S. Moore Sapindaceae Weeping pittosporum DUHO Duboisia hopwoodii (F. Muell.) F. Muell. Solanaceae Pituri Trema tomentosa (Roxb.) Hara var. viridis (Planchon) TRAS Hewson Ulmaceae CLFL Clerodendrum floribundum R. Br. Verbenaceae Smooth spiderbush / Yala CRSP Cratystylis A36062 Glen Helen PLME Plectrachne melvillei C. Hubb.
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