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REMOTE SENSING FOR CHARACTERIZATION OF GRANITE OUTCROP HABITATS IN SOUTH-WESTERN AUSTRALIA

A.G.T. Schut Curtin Institute for Biodiversity and Climate, Department of Spatial Sciences,

Curtin University GPO Box U1987 Perth, WA 6845

Ph: +61 8 9266 2691; Fax: 61 8 9266 2495 t.schut@curtin.edu.au

G.W. Wardell-Johnson

Curtin Institute for Biodiversity and Climate, School of Science, Curtin University

C.J. Yates

Science Division, Dept. of Environment and Conservation

G. Keppel Curtin Institute for Biodiversity and Climate, School of Science, Curtin University

C. Earls

AAM Pty, Ltd

S.E. Franklin

Trent University 

Abstract Granite outcrops provide a wide range of habitats fostering biodiversity. They may also serve as islands of fertility in nutrient deprived landscapes. The wide range of soils and micro-climates on these outcrops in combination with water and nutrient accumulation at the footslopes means that these outcrops may act as areas of refuge when conditions are unfavourable elsewhere. To better understand the ecosystems present on these granite outcrops and their role as refugia in times of past and future climate change, a total of 28 outcrops were selected across the rainfall gradient of south-western Australia. These included: Mt Cooke, Mt Stirling, the Humps and Chidarcooping to the east of Perth; Mt Chudalup, Mt Frankland, Mt Lindesay, the Porongurup ranges and Mt Manypeaks in the south near Albany; and the area around Peak Charles/Peak Eleanora and Mt Arid near Esperance in the south-east. Multiple return LiDAR data and high resolution imagery was acquired in late February 2010 and geo-referenced to 0.15 m vertical accuracy. For the larger outcrops, mean distance between LiDAR pulses was 1.2 m at the surface, but higher densities were achieved on smaller outcrops. Multiple return LiDAR data is used to create a high resolution digital elevation map to characterise outcrop morphology and flow patterns in detail. Also, fused imagery in combination with LiDAR return characteristics will be used to map ecosystems on all outcrops, providing means to link outcrop characteristics to ecosystems. A selected number of outcrops will be studied in detail with ecological surveys and distributed climate data loggers to better understand relationships between ecosystems, outcrop morphology and microclimate.

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In this paper, first results are presented for the remote sensing component of the project. The role of LiDAR and combinations of multi-temporal and multi-spatial remote sensing datasets for characterization of these granite outcrops is discussed.

Introduction South-western Australia is Australia’s only global biodiversity hotspot (Myers et al. 2000). The Earth’s biota is facing profound risks of damaging climate change during this century (IPCC 2007). The south-west of West Australia (SWWA) area is already experiencing climate change, with a strong reduction in rainfall, attributed in part to global warming, and it is predicted to become warmer and dryer in the near future (CSIRO 2007, Smith et al. 2007a, Bates et al. 2008). Genetic studies indicate that many taxa have survived past regional climate changes by contracting to dispersed refuges (Yates et al. 2007, Byrne and Hopper 2008). Such refuges will likely contribute to the persistence of species and ecological communities under anthropogenic global warming. The precise nature of these refuges and where they occur remains unknown, but it is likely that environments that facilitate habit stability will be important. The relatively subdued ancient landscape in SWWA, with the exception of the Stirling Range, offers little scope for biota to migrate to altitudinal refuges. The regions topography is however substantially influenced by the Yilgarn Craton and where erosion exposes the bedrock granite outcrops rise above the landscape creating topographic complexity which influences resource flows and microclimates. Numerous granite outcrops and inselbergs are scattered across the mesic to semi-arid rainfall gradient and are a conspicuous feature of the SWWA landscape (Withers 2000). Granite outcrops provide a wide range of habitats, are rich in biodiversity and foster many endemic plant species. In SWWA, more than 17% of the State’s vascular flora is represented on granite outcrops and associated environments, including c. 10% of the State’s threatened flora (Brown et al. 1998). In addition, many species in the wider landscape are found in plant communities fringing these outcrops and many of these species have been restricted to the granite outcrop environment during past climate change (Yates et al. 2007, Byrne 2008, Byrne and Hopper 2008). One reason for the disproportionately high number of species associated with granite outcrops, in comparison with the surrounding landscape matrix, lies in the variety of microhabitats that they provide (Hopper et al. 1997, Porembski et al. 1997). This suite of unique microhabitats includes: rock pools (gnammas), non-flooded soil pockets, vegetation mats, temporary flushed rocky surfaces which provide ephemeral habitat (cyanobacteria, fungi (lichens), and mosses) and aprons with influx of water and nutrients from the outcrop. We aim to provide an overview of data collected to characterize granite outcrops, habitats and the ecosystems present in South-Western Australia. An overview of remote sensing approaches to study biodiversity and habitats is also provided, including a discussion on the role of remote sensing technologies for refugia characterization.

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Figure 1. Rainfall isohyets indicating annual long-term means of precipitation for SWWA.

Materials and Methods South-western Australia has a strong rainfall gradient, ranging from mesic to dry from the SW corner in a NE direction (see Figure 1). A total of 28 granite outcrops have been selected for the study, covering the rainfall gradient from the south-west corner to the north-eastern parts of SWWA. The outcrops selected were located within a path that could be covered by three 4 hour flights leaving from Jandakot (Perth) and Albany. A few other important outcrops were scanned when transferring the airplane from Esperance to the east coast. The flight time available and the area to be covered for each outcrop determined the flying height and thus the density of laser strikes on the ground. Outcrops areas were scanned from approximately 2 km altitude, covering a swath of approximately 1500 m wide. Large outcrops in low and high rainfall zones were included and smaller outcrops in the flight path were scanned when the opportunity was present. The aircraft was flown between the 27th of February and 2nd of March 2010. The aircraft carried an Optech ALTM3100EA airborne laser scanner. The average distance between laser strikes on the ground was approximate 1.2 m, with a vertical accuracy of 0.15 m and horizontal accuracy of altitude times 1/5500, resulting in accuracy better than 0.35 m. A total of up to 4 returns were recorded for each pulse. The intensity of the return was also recorded. As a first step, laser strikes were classified as ground or non-ground. From this, a DEM is extracted for selected outcrops. The vegetation height was derived from extracted first return laser strikes classified as non-ground. RGB imagery was acquired simultaneously with a DSS439 camera (Applanix) and 60 mm lens recording 16 bit imagery. The spatial resolution of images

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acquired was about 0.2 m at nadir. Radiance was recorded within a spectral range of 400-500 nm (Blue band), 500-600 nm (Green band) and 600-700 nm (Red band). Reflectance of a selected area with lichen and algae typical for granite outcrops was measured in two transects on Mount Stirling (31.8oS, 117.6oE) on 18 March 2010. The radiances were recorded with an ASD Fieldspec Pro Jr spectrophotometer which covers the spectra range between 350-2500 nm with a 3-30 nm bandwidth. Reflectance was calculated using radiance recorded from a Spectralon calibrated 50% reflection panel. Spectra were recorded from a height of 1.7 m above ground, using a fiber-optic cable with a field of view of 8o. Means of 10-30 readings were calculated and stored for each sampling point. For each sampling point a description was recorded of the plant/algae/lichen present and a geocoded digital image was taken.

Results Figure 2 shows the locations of all outcrops scanned in February-March 2010. The outcrops selected included granite outcrops in the SW near Albany: Mt Chudalup, Mt Frankland, Mt Johnston, Mt Lindesay, Mt Roe, Mt Romance, Crossing Hill, the Porongurup ranges and Mt Manypeaks. Near Perth, Mt Cooke was scanned. In the area east of Perth, outcrops included the Chidarcooping, Mt Stirling, Mt Caroline area, Kokerbin Rock and the smaller outcrops King Rocks, Pony Hill, Mt Talbot, The Humps and Twine reserve. In the Albany-Esperance area, Dunn Rock, Purnta Rock, Dragon Rocks, Dog Rock, Peak Eleanora & Peak Charles area, Mt Bearing, Boyatup and Mt Arid.

Figure 2. Locations of 28 selected outcrops in the SWWA area. White areas are cleared, green areas contain native vegetation.

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Figure 3. Geocoded images of the top of

Mt Stirling

Figure 4. Reflectance and derivative reflectance of selected ephemeral species

on Mt Stirling.

Remote sensing and biodiversity

Granite outcrops are rich in areas with shallow soils providing habitats for a wide range of biota including vascular plants. However, rock surfaces provide ephemeral habitats for many species of lichen, algae and mosses. Typically there is little uncovered rock present. Figure 3 shows the wide variety of habitats near the top of the Mt Stirling granite outcrop. The image at the top (Fig. 3a) shows a patch with deeper soil providing sufficient substrate for shrubs and other vascular plants. The areas without soil include some uncovered granite rock surfaces on loose fragments, but also many ephemeral habitats with red algae, blue/green lichen and black mosses. A shallow soil with some smaller vascular plants is visible on the centre left of the

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centre image (Fig. 3b), and some higher plants on a patch with deeper soil. In the lowest image (Fig. 3c) a vegetation mat is visible, providing an unstable substrate, rich in organic matter with annual plants and mosses. Quantification of the abundance of granite outcrops at the surface therefore depends on recognition of these habitats. The reflectance spectra recorded at Mt Stirling (Fig. 4) indicates that the presence of ephemeral plants changes the spectral appearance, lowering the reflectance in the visible range strongly (Satterwhite et al. 1985) and masking absorption features in the infra-red range. The LiDAR data provide an excellent DEM suitable for detailed mapping of water and nutrient flows (Fig. 5). The vegetation height shows that taller vegetation is present in the valleys and near the aprons of these outcrops, indicating more abundant resources than in the surrounding landscape.

Figure 5. Vegetation height above the surface (in meters) draped over the DEM at Mt Chudalup (5a), Crossing Hill (5b) and Peak Charles (5c). Elevation is exaggerated 3-

times.

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Figure 6. RGB image of the western side of Mt Stirling, showing details of habitats with small trees and bushes, shallow soils with some bush plants and areas with lichen and

algae. The RGB imagery collected show an enormous wealth of information on habitat type, plant coverage, size and location (Fig. 6). Discussion

Remote Sensing and biodiversity

At the landscape level, heterogeneity in ecosystems and vegetation composition indicates the presence of a wide range of habitats due to the direct links between rock, soils and resource availability (Dubbin et al. 2006, Pepper et al. 2008). Three main approaches based on remotely sensed data are used to quantify heterogeneity and biodiversity at various scales (Nagendra (2001): 1) direct mapping of species occurrence; 2) classification of habitats and prediction of biodiversity based on habitat requirements; 3) relationships between spectral radiance and species distribution patterns. The second approach has been used in many studies including those using metrics to quantify landscape patterning and fragmentation (Nagendra et al. 2004, Wulder and Franklin 2007, Gillespie et al. 2008). The third approach is promising especially at more detailed spatial scales (Rocchini et al. In Press). The availability of LiDAR data opens up other avenues to quantify biodiversity, providing a means to characterise structural attributes of the canopy (St.-Onge et al. 2003, Koukoulas and Blackburn 2005b, Goetz et al. 2007, Wulder et al. 2008, Miura and Jones 2010).

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Remote sensing of granite outcrops

The microhabitats of granite outcrops are linked to the abundance of granite at the surface in the typical granite domes. Although the location of major granite outcrops is known generally, detailed maps of granite rock abundance at the surface are not yet available. Granite abundance may be mapped with Landsat TM or ASTER data (Campbell et al. 1999, Rowan and Mars 2003, Watts et al. 2005). However, spectral characteristics are dominated by lichen and algae when mapping granite abundance at the surface on outcrops, in the visible and near infrared wavelength range (Satterwhite et al. 1985) but also in the mid infrared range masking granite absorption features.

A remote sensing approach for Refugia

A remote sensing approach to identifying pattern and process in refugia involves purpose-defined data acquisition and processing approaches and could consist of five hierarchical analyses. 1) Evaluation of phenological and productivity responses to changes (e.g.,

rainfall patterns or fire regimes) on long time-series imagery (Hill and Donald 2003, Heumann et al. 2007, Smith et al. 2007b), on various spatial scales. At the regional scale, response may indicate adaptation of vegetation to changes in the length of growing seasons. At the local scale, multi-temporal imagery from e.g. MODIS can be used to compare seasonality signals of aprons with vegetation in the surrounding landscape. Imagery of higher resolution sensors, e.g. Landsat TM, may be required to extract information at a scale that allows identification of these apron areas, through image fusion techniques (Pohl and van Genderen 1998, Zurita-Milla et al. 2008, Hilker et al. 2009);

2) Usage of medium spatial resolution sensor data (e.g., Landsat) to relate ecosystem processes, heterogeneity structure and health at the landscape scale for determination of environmental correlates that explain and predict pattern and process in refugia (Song et al. 1997, Wulder and Franklin 2007). For the SWWA, this can be used to identify and characterise areas with high habitat diversity and understand the relationships between habitats and surrounding landscape, essential to predict if these habitats have acted as refugia or are likely to be a refugium in times of climate change. For this, indices and metrics need to be derived from imagery at the landscape scale that correlate strongly with indicators of habitat diversity, building on the approaches as summarised by i.e. Rocchini (In Press) and Nagendra (2001);

3) Usage of high spatial resolution multispectral/hyperspectral imagery and LiDAR to detect, characterize and monitor specific habitats and/or components of ecosystems for condition and change (e.g., density, overstory/understory, crown shape and leaf area) (Miura and Jones 2010). From these, water and resource flows on and near granite outcrops can be mapped and modelled in high detail to quantify environmental variates (solar irradiation, aspect, slope) using a high resolution DEM. This will provide a better understanding of the richness and functioning of habitats and ecosystems in general and specifically on Granite Outcrops. These environmental variates in combination with structural information of the

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canopy (St.-Onge et al. 2003, Goetz et al. 2007, Miura and Jones 2010) and high resolution imagery (Bork and Su 2007, Asner 2009, Kim et al. 2009, Erdody and Moskal 2010) provide a very detailed view on the vegetation present, giving opportunities to quantify species diversity within delineated habitats;

4) Fusion of complementary LiDAR data with aerial optical imagery to produce accurate maps and digital elevation models (DEM) linking canopy height, structure and spectral characteristics to species composition and biodiversity indicators derived from on-ground surveys (Koukoulas and Blackburn 2005a, Erdody and Moskal 2010). An accurate characterization of habitats allow biologists and ecologists to identify biodiverse habitats that require further study and may assist in identifying potential refugia;

5) Usage of indicator responses at site level to understand changes at the landscape level, indirectly by modelling approaches (e.g. niche modelling approaches) or directly utilizing concepts of sensor fusion (Roy et al. 2008, Hilker et al. 2009). Promising indicators derived from high resolution airborne images may correlate to spectral bands or indices from lower resolution sensors, providing means to upscale field sampling results.

Conclusion Remote sensing can be used to understand patterns and processes in relation to refugia on granite outcrops by deriving multispectral views of the environment at multiple spatio-temporal scales, readily integrated with other forms of data (e.g., GIS layers and georeferenced observational data). It provides means to characterize ecosystem dynamics and indicators to quantify ecosystem, habitat and microhabitat diversity. From this, in combination with ecosystem and niche modelling approaches, habitats and micro-habitats that are more likely to foster refugia can be identified, mapped, studied and monitored.

Acknowledgements The authors acknowledge the Department of Environment and Conservation, AAM Ltd Pty and the ARC for their support in the linkage project (LP0990914) entitled: ‘Protecting the safe havens: will granite outcrop environments serve as refuges for flora threatened by anthropogenic climate change?’.

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