VEGETATION AND SOIL MAPPING MT BUNDEY TRAINING AREA. Brian Tunstall, Tony Orr, and Alan Marks

Transcription

1 VEGETATION AND SOIL MAPPING MT BUNDEY TRAINING AREA Brian Tunstall, Tony Orr, and Alan Marks Technical Report 8/98 March 1998

2 VEGETATION AND SOIL MAPPING MT BUNDEY TRAINING AREA Brian Tunstall 1, Tony Orr 2, and Alan Marks 1 Environmental Research and Information Consortium Pty Ltd, PO Box 179, Deakin West ACT Military Geographic Information Unit, Larrakeyah Barracks, Darwin NT 0820 CSIRO Land and Water Technical Report 8/98 March 1998 Acknowledgements This study was funded by the Department of Defence Statistical analyses by Geoff Wood, Biometrics Unit, CSIRO Information and Technology, Canberra 1

3 TABLE OF CONTENTS INTRODUCTION 3 BACKGROUND 5 LOCATION AND LAND USE 5 CLIMATE 5 TERRAIN 6 GEOLOGY 6 SOILS 8 VEGETATION 10 METHODS 12 SATELLITE IMAGE PROCESSING 12 RADIOMETRIC MAPPING 13 FIELD SURVEY 14 ANALYSIS 17 RESULTS 19 VEGETATION 19 SOILS 22 DISCUSSION 26 DEVELOPMENTS 28 REFERENCES 29 ANNEX 1 ANNEX 2 2

4 INTRODUCTION As military training often impacts on the natural environment, there is a need to ensure that use of training areas is sustainable. While impacts can be repaired, a more cost-effective approach is to reduce impacts and allow for natural recovery. This can be done by providing environmental information that supports military training, where this approach benefits the environment, increases the effectiveness of training, and provides cost savings (Tunstall, 1996). Military training usually requires purpose-specific information, such as the location of quarry sites for road construction (Tunstall and Marks, 1997) or suitable areas for parachute dropzones (Gourlay, et al.1996). Such information is usually produced in response to a specific need, but the ease of collecting the appropriate information varies widely. Time and money usually preclude one-off purpose-specific studies, and so the information must be derived from existing data and maps. The alternative is to create a detailed information bank on an area s natural resources which, with suitable interrogation, can answer mapping needs as they arise. To assist in land use planning, several studies have been undertaken to define and map the natural resources around the Mt Bundey Training Area (MBTA). Apart from geology, the studies have focused on vegetation and soils, as these most affect broad-area land use. However, these studies have mapped landscape patterns rather than individual resources, making it difficult to use the information in management. Previous natural resource mapping studies used variations of the Land Systems approach (Christian and Stewart, 1968) to map broad landscape patterns identified by similarities in geology, terrain, vegetation, and soils. All were developed for planning, and mapped mixed classes rather than the entities of interest, and they generated boundaries through visual interpretation of remotely sensed imagery. The basic approach reflected the national development needs of the 1960 s and the technology then available for mapping. The regional land resource maps covering MBTA include geology, land systems (Storey et al., 1969, 1976), and vegetation (Wilson et al., 1990). Additional studies specific to MBTA provided environmental information for its development as a military training area (Kinhill, 1991, Anderson and Tunstall, 1992). The geology maps provide the greatest detail, but even so the mapping mainly represents general categories identified through visual interpretation of the landscape from aerial photographs. Current needs for natural resource information differ in that sustainable development depends on management as well as planning. Maps of individual resources are now needed to show a level of detail appropriate to environmental management. Also, use of a common map base, as done in the land systems approach, is inappropriate, as spatial data now derive from Geographic Information Systems (GIS) where application occurs through analysis of base information for different resources. Such analysis requires independence in the derivation of the base map information, and prevents the generation of different maps from common information. For example, analysing the relationship between vegetation and soils cannot sensibly be undertaken where information on vegetation has been used to map the patterns of soils. The technology for mapping and spatial data analysis has changed dramatically since the 1960s, with a grid-cell approach made practical by the availability of digital data in raster format. We can now map fine patterns over large areas using numerical techniques quicker and easier than past visual analysis, which was limited to coarse patterns. No longer do we need to visually draw large polygons, nor make assumptions concerning composition. Nevertheless, difficulties 3

5 remain in obtaining the required discrimination of the entities of interest, and in maintaining independence in the derivation of the different mapped layers. Numerical analysis of satellite imagery provides opportunities for mapping land-cover patterns at high spatial resolution, but translation to a vegetation map requires removal of ambiguities arising from different vegetation having equivalent spectral signatures. For example, discrimination of eucalypt forests composed of different species is seldom possible except where associated with a structural difference. This limitation can arise for several reasons, such as communities being composed of heterogeneous mixtures of species, and usually cannot be overcome by reference to the image data alone. The effort required to remove such ambiguities depends on the characteristics of the area, and the proposed application of the results. Airborne measurements of gamma radiation emissions (radiometrics) can be processed to provide information in a similar form to satellite imagery, but here the data reflect the parent material and degree of weathering of the surface 30 cm of soil rather than land cover. These data can therefore be used to map soils, and their use is significant on several counts. It potentially allows mapping of soils without reference to information on terrain or vegetation, and therefore allows independence in the derivation of these map layers to be maintained. It also allows mapping of soil properties, as opposed to soil types, which facilitates application in land use and management, as well as testing of map reliability. A detailed soils map can be efficiently produced where all mapped classes are significantly different at the 95% confidence level (Tunstall and Gourlay, 1994). The objective of this study was to produce, at the highest level of detail practicable, maps of vegetation and soils suitable for use in management. Practicality in map production as well as application dictated the use of remotely sensed data. However, these techniques demand suitable data. Numerous cloud-free satellite images are available for MBTA, but most contain pronounced patterns due to fires. Two Landsat TM images, essentially free of fire scars, currently exist, and these can be used to identify patterns of vegetation with little interference from past burns. One, however, was acquired in the middle of the wet season, and so evinces patterns due to flooding. The MBTA radiometric data are suitable for soil mapping but were acquired for mineral exploration, and so were limited to a km block of the training area. This represents around two-thirds of the MBTA and, which demonstrates the technique by providing information for the bulk of the training area. 4

6 BACKGROUND Location and Land Use The 100,000 ha MBTA is located around 120 km south-east of Darwin, south of the Arnhem Highway between the Mary River and the Kakadu National Park. The prior land use commercial grazing of the native vegetation ceased in the mid 1980s, and the property was acquired by the Commonwealth of Australia for use as a military training area in An Environmental Impact Statement (EIS) for the proposed land use was submitted in 1991 (Kinhill, 1991), and the area became available for training in The MBTA was primarily acquired for training by the 2nd Cavalry Regiment and NORFORCE, but was to include other units providing support, including Armour. The usage envisaged was: 2nd Cavalry Regiment (approximately 19 weeks per year) NORFORCE (approximately 12 weeks per year) battalion group (approximately 6 weeks per year) elements of brigade-level training every three years. The eastern part of MBTA forms part of the upper reaches of the Wildman River catchment, which is registered on the National Estate because of extensive wetlands. The MBTA is remote from the wetlands that form the basis for the listing, but represents a significant 26% of the catchment. The associated requirement for environmentally sustainable use is reinforced by the eastern boundary of MBTA abutting Kakadu National Park, and the western areas forming part of the Mary River floodplain. Climate The climate broadly subdivides into the wet and dry periods of November to March, and April to October, respectively. Solar radiation and ambient temperatures are generally high. Maximum daily temperatures average around 37 C in October/November, and around 32 C May through July. Minimum temperatures average around 24 in November/December and 18 in July through August. The seasonal range in temperature is only 5.6 degrees, but October/ November is by far the most uncomfortable period with September/December being only marginally better. This arises because of the high levels of relative humidity and solar radiation, as well as high ambient temperatures. The index of relative humidity averages around the high fifties July through September, increasing through October/November to 80% for the period January through April. The annual rainfall averages around 1330 mm with the 10 and 90 percentiles being around 1000 and 1800 mm respectively. The variability between years for annual and monthly averages is low by Australian standards, but rainfall is highly seasonal with over 90% of the rain falling in the wet season of November to March. Some rain can be expected in October and April, but May through September is virtually rainless. October/November, and May are transitional between the wet and dry. The annual potential evaporation averages 2200mm, and hence greatly exceeds precipitation. It is lowest during the wet season, and highest in August through November. The patterns of 5

7 rainfall and potential evaporation result in seasonal waterlogging of soils. The duration of waterlogging is affected by local factors such as surface redistribution of water, uptake by plants, and vertical and lateral sub-surface seepage, but these modifying effects are small by comparison with climatically induced changes. Atmospheric temperature inversions occur during the dry season through radiant cooling of the earth s surface at night. These have little direct effect on comfort, but are of significance in the dispersal of airborne pollutants. Smoke from the multitude of fires over the dry is trapped close to the surface, and forms a blanket of haze over the country. This smoke can affect comfort, create a hazard to light aircraft, and degrade the quality of remotely sensed imagery. Terrain The training area subdivides into three main geomorphologic zones. The western side of the training area has levees and floodplain associated with the Mary River, and is flat with occasional emergent outcrops of siltstone/sandstone. This plain is bounded on the east by a band of hills with a generally northerly strike composed of Proterozoic siltstone/sandstone, moderately to steeply undulating, dissected by a series of flat bottomed floodways. The terrain east of these hills is slightly undulating, and is composed of the tertiary Wildman and Mundogie sandstones/siltstones. The low rises often contain outcrops of lateritic rock or angular sandstone gravel, but the long slopes and broad swales typically have a surface lag of rounded ironstone gravel. The landscape around the hills and floodplain can be broadly subdivided into areas of erosion and deposition. Slopes of 2 and greater are generally actively eroding while those of 1 and less are generally accumulating sediments. Slopes around 1.5 may be eroding or accumulating sediments depending on the characteristics of the flow of water over the surface. Laminar flow decreases the likelihood of erosion, and this occurs on smooth surfaces. Turbulent flow increases the likelihood of erosion, and this occurs where surface rocks or depressions cause sudden changes in the direction of flow of water. The crests, and upper and mid slopes of hills are actively eroding, and the soil surface is typically covered by a lag of angular sandstone gravel 1 to 5 cm in diameter. The lower slopes are generally colluvial with the size of gravel in the surface lag being smaller than upslope. The natural angle of repose of the sandstone gravel has been given as 11. The eastern portion of the area does not exhibit a simple relationship between terrain and surface geology. The spatial patterns are complex because of reworking of the material. The material on the hills may be derived from a lateritic rock, a pallid zone, or sandstone. Soils in locations having low elevation and slope may be derived from in situ weathering of either detrital lateritic hardpans or sandstone, or they may be colluvial or alluvial. These complex patterns are best interpreted in terms of the development of lateritic formations. Geology Most of the geology of the area is given in 100,000 map sheets (Stuart-Smith et al., 1984a, l984b, 1986), but the top right corner is only mapped at 1:250,000 scale (Needham, 1984), resulting in much lower resolution for this area. The interpretations and unit boundaries vary between maps, but a composite map can be compiled (Fig. 1). The units identified in geology maps primarily define 'hard rock' geology, as the intent is to identity the location, extent and relative positioning of geological formations. However, given the extent of weathered surface material in the region, an attempt was made to associate 6

8 Cainozoic sediments with their parent rock, thus the geology map partially represents an interpretation of the weathered mantle (regolith). The major geological formations, such as the Wildman Siltstone, are associated with major events causing faulting and uplifting. However, minor events have also caused significant changes to surface geology. Young detrital lateritic rock derived through weathering and erosion of the original Wildman Siltstone occurs within that formation. In places, the original soil surface covering the detrital rock has been stripped so that the current soil surface is derived from weathering of the detrital layer. Such complexity also exists within the area designated as the Mary River floodplain, as the materials have been derived from local formations as well as deposition from the river, and soils are variously derived from coarse sands, loams, silts, and heavy clays. The formations contributing to sediments have generally not been identified, particularly for the Mary River floodplain. However, the associating of sediments with parent material has been done with the Wildman Formation (Czl / Ppw) because of the extensive occurrence, and small occurrences of sediments derived from the Petrel Formation (Czs, Csl / Kp) have also mapped. Further regolith mapping has also been attempted by the subdivision of Tertiary and Quaternary sediments. Summary descriptions for the mapped categories of unconsolidated sediments are given in Table 1. However, the descriptions given for the Cz category (Table 1) refer to soil types rather than the nature of the material, where these soil types occur throughout most of MBTA. The boundaries between exposed rock formations in MBTA are usually well defined through being expressed in the relief or terrain, and the boundaries between the alluvial/colluvial deposits and rock outcrops can also be well defined in this highly eroded landscape. However, the boundaries between the different alluvial/colluvial deposits are poorly defined through having little expression in the terrain. Consequently, many of the geological boundaries mapped for MBTA have been derived by visual interpretation of vegetation patterns. Such derivation of geomorphologic categories from visual interpretation of vegetation produces maps of uncertain significance and reliability, as evidenced by three categories of Cainozoic formations not always being separated. The geological category Czs/Czl is widespread throughout the training area. Table 1Categories of unconsolidated sediments mapped for MBTA CzLithosols, gradational red soils and yellow earth type soils CzaWinnowed sand, silt, clay CzsQuartz sand, ferruginous and clayey sand: fan deposits CzlDetrital, pisolitic and concretionary ironstone CzgHigher level gravels and gravely lithosols CZLSandstone, and metasediment fragments, sand: talus and scree deposits QaSilt, sand, clay; locally consolidated grey sandy siltstone along some drainage courses: creek and river alluvium QsUnconsolidated sand: outwash and colluvial deposit QalSilt, clayey silt: levee deposit 7

9 Soils Soil Types Five soil types were described for the area in the EIS (Kinhill, 1991); lithosols, red and yellow earths, soloths and levee soils (Fig. 2). The term levee indicates position in the landscape and only partially relates to the process of soil formation. The terms soloth and lithosol relate to the process of soil formation. The term earth describes the pattern of change of texture in the profile. The soils have been described using a mixture of classification systems, and this makes interpretation difficult. Examination of the soil map (Fig. 2) indicates that levee soils have essentially been excluded from the training area, while red earths have been restricted to the area surrounding the billabong on the Jim Jim Road. The soils in the training have therefore effectively been mapped within three categories that represent the Mary River floodplain (soloths), floodplains associated with some drainage lines (yellow earths), and the remainder (lithosols). The Land Systems reports (Storey et al., 1969, 1976) provide more detail on the soils than the EIA. The soil types mapped as occurring in MBTA are described below by way of Major Soil, Soil Family, Great Soil (Stace et al., 1968), and Northcote codes (1979). Profile descriptions from the Land System Reports are also given, but have been revised to accord with the particle size distributions given in their analyses, and the guidelines of McDonald et al.(1984). While the distributions of the soils were not mapped, approximate percentage occurrences within the training area can be deduced from the Land System maps and accompanying descriptions. Depths are in cm. Uniform coarse textured, no pedologic organisation. Skeletal Skeletal (lithosol). (33%) Uc1, Um1. Hills and crests. Uniform profile of gravelly loamy sand with a surface lag and high content of angular gravel. Howard Alluvial soils (sand). (3%) Uc1.21, 1.22 Alluvial flats. A, 0-120, dark greyish brown sand or loamy sand becoming yellowish or reddish with depth. D, , light brownish grey sandy clay loam, common weekly cemented mottles. Nourlangi Alluvial soils (sand). (3%) Uc1.21, 1.22 Alluvial flats. A, 0-70, dark grey loam or sandy loam grading to greyish brown sand, rare nodules. B, gradational, non calcareous with earthy fabric D, , grey sandy clay, abundant carbonate, medium amounts of nodules. Cahill* Lateritic Red Earth. (20%) Gn2.11, 2.21 Undulating lowlands. A, 0-60, greyish brown loamy sand A1 with yellowish grey loamy sand A2. B, , dark reddish brown silty clay loam. * The descriptions differ between Land System Reports. Hotham Lateritic Red Earth (2%) Gn2.11, 2.12, 2.14, Undulating lowlands. A, 0-30, dark red loamy sand with high pisolitic gravel content. B, , dark reddish brown loamy sand with high pisolitic gravel and lateritic fragments. B-C, , dark reddish brown gritty loamy sand with lateritic fragments. 8

10 Mummarlary Lateritic Red Earth (7%) Gn2.11, 2.12, 2.14, 2.21, Undulating lowlands. A, 0-90, dark greyish brown loamy sand A1, yellowish brown sandy loam A2. B, , yellowish red sandy loam with high amounts of pisolitic and/or angular gravel. C, dense lateritic nodules and/or strongly weathered rock. Woolner Lateritic Red Earth (9%) Gn2.41, 2.42, Undulating lowlands. A, 0-40, dark greyish brown gravelly loamy sand A1, yellowish brown gravelly A2 (pisolitic). B, , brown mottled gravelly sandy loam grading to reddish brown loamy sand. Elliot Yellow Earth (6%) Gn2.61, 2.62, 2.81, Depressions and colluvial foot slopes, floodways and floodplains. A, 0-30, greyish brown sandy loam A1, yellowish brown A2. B, 30-60, yellowish brown clay loam with some cemented mottles. C, extension of B, or hard red sandy clay loam. Koolpinyah Lateric Yellow Earth (9%) Gn2.21, 2.61, Alluvial flats and floodplains. A, 0-50, dark greyish brown gravelly sandy loam A1, yellowish brown A2, pisolitic gravel. B, , brown to yellowish red gravelly loamy sand, reddish mottles, medium gravel. C, , as for B but with angular gravel from parent rock. Cullen Yellow Earth (5%) Gn2.81, 2.91 Lower colluvial slopes, floodways. A, 0-40, dark grey coarse loamy sand A1, with pale grey A2. B, , yellowish sandy loam, mottling common. Gradational, non-calcareous, smooth ped fabric. McKinlay Yellow Earth (2%) Gn3.53, 3.73, Alluvial flats and floodplains. A, 0-40, black silty loam A1, pale brown loamy sand A2. B, , yellowish brown clay loam, brown loam to brown sandy loam, strongly mottled. Duplex (texture contrast) Margaret Solodized Solonetz (1%) Dy3.33, 3.23, Floodplains. A, 0-30, dark greyish brown sandy clay loam, some rounded nodules on the surface of B. B, , grey or yellowish brown light clay, columnar structure, yellowish red mottle in B2. B-C, 125+, similar to B but lighter (sandy clay) texture. These can be summarised as: Lithosol (33%) Lateritic Red Earth (38%) Yellow Earth (22%) Sand (6%) Duplex (1%) The results from the Land Systems studies are comprehensive, but omit significant soils, and do not map distribution. 9

11 Soil properties Chemical and physical properties of the soils provided in the Land Systems Report for Alligator River are given in Table 2. The soils are mainly coarse textured reflecting the parent materials, being generally composed of around 10% clay, 5% silt and 85% sand. The sand is generally angular with equal proportions in the and mm size classes. Clay contents in subsoils analysed rise to a maximum of around 30%, but many profiles average less than 4%. All soils are slightly acid, except for the floodplains where accumulation of calcium and magnesium salts has produced some alkaline B horizons. All soils have low organic carbon and nitrogen. The levels are highest in the surface soil, but these are still low and vary considerably between soils. The surface organic carbon levels likely reflect recent fire history, and so need not be characteristic of the soils. The levels of phosphorus are generally between 5 and 10 ppm, which is low. The Woolner lateritic red earth, which has a high content of pisolitic gravel, has negligible phosphorus. Specific conductivities (EC) are low but are highest in the depositional, seasonally inundated soils. These depositional soils show accumulation of salts in the top and bottom of the profile. Sodium levels are negligible reflecting the nature of the parent material and high leaching. Potassium levels are slightly higher, but only the Koolpinyah lateritic yellow earth has appreciable levels. All depositional soils show significant calcium, while all soils have significant magnesium. Vegetation The vegetation information in the EIS for MBTA was mapped by the Conservation Commission of the Northern Territory (Wilson et al.,1989). Nine vegetation types were mapped within MBTA using visual interpretation of aerial photography (Fig. 3), with two of these representing point features. The vegetation on the hills (skeletal soils) is given as Eucalyptus tintinnans / E. dichromophloia low woodland. The vegetation on the Mary River flood plain mainly mapped as Melaleuca viridiflora / Eucalyptus polycarpa low woodland and/or Eriachne burkittii grassland, but includes the categories of Eucalyptus spp. low woodland, and Eucalyptus bigalerita woodland. The vegetation east of the hills is primarily identified as Melaleuca viridiflora / Eucalyptus polycarpa low woodland and/or Eriachne burkittii grassland, and Eucalyptus spp. low woodland, and hence is largely the same as the two predominant vegetation on the Mary River flood plain. However, appreciable areas of Eucalyptus tetrodonta woodland were mapped in the north of the area either side of the Jim Jim road. All vegetation types identified are widespread across the Northern Territory but some, such as billabongs and monsoon vine-thicket, have limited extent. 10

12 Table 2.Chemical properties for soils in the Mt Bundey Training Area, derived from the Lands Systems reports. Sample Depth Particle Size Distribution (% < than) phecorg. CCEC Exchangeable Cations (m equiv. %) (cm) u S %m eq %NaKCaMg P ppm Skeletal - Skeletal (lithosol) Cahill Brown soils of light texture (earthy sand) Koolpinyah Lateritic Yellow Earth Cullen Yellow Earth Mc Kinlay Yellow Earth Hotham Lateritic Red Earth Woolner Lateritic Red Earth

13 METHODS The vegetation patterns were mapped using numerical analysis of satellite imagery and the soils by numerical analysis of airborne measurements of gamma radiation (radiometrics). Satellite Image Processing A subset of a 7 band quarter scene of Landsat TM image was registered to the 100,000 topographic maps with and RMS error around 30 m using features on the map that could be readily identified in the imagery. The image was then classified to identify land cover classes using an iterative procedure using MIPS GIS, microbrian image analysis software. Some class reassignment was conducted using ERMapper. Around 200 classes were automatically generated using narrow limits for the variance in an unsupervised maximum likelihood classification. The classes were then iteratively amalgamated, firstly according to spectral similarity, and then according to spectral similarity and spatial adjacency, to produce a land cover classification that summarised the main patterns in the area using a manageable number of classes (17). The measures of similarity were Ward s incremental sum of squares for spectral, and co-occurrence for spatial (Tunstall et al, 1984). Use of the large number of classes circumvents limitations in the spectral statistic arising from assumptions concerning distributions of values about the mean, but these limitations increasingly become limiting as the class number declines below 100 through class aggregation. The spatial statistic has little value with high numbers of classes, but provides useful information on context as classes are aggregated. The spatial statistic compensates for deficiencies in the spectral statistic, and improves the discrimination of features. The satellite imagery provides high discrimination of spatial patterns of land cover, but does not always discriminate between the features of interest. Ambiguities arise because different vegetation types can have equivalent spectral signatures, and because the illumination angle introduces terrain related patterns. The terrain affects the spectral signature by modifying the proportions of sunlit and shaded foliage, and in steep terrain the vegetation may be completely masked by shadow. Discrimination of the features of interest can be improved by constraining the extent of the analysis, as with limiting the analysis to a block containing the MBTA. Further subdivision could have been applied, as with separately analysing the main environmental regions such as the flood plain and hills, but this is not needed to obtain the required detail in spatial pattern. Rather, a sub-division was applied after the initial analysis when applying labels (descriptions) to the mapped land cover classes. The main ambiguities in relating vegetation types to the mapped land cover classes were removed by subdividing the area into three regions or zones, represented by the hills, the Mary River flood plain, and the eastern plains. Labels were then ascribed according to the zone as well as land cover class, which resulted in the splitting of some classes within zones, and the aggregation of others. These zone boundaries represent general regions, and are not precisely located according to objective criteria. 12

14 Radiometric Mapping Data Radiometrics provide measurements of the emissions of gamma radiation from the earth's surface, typically partitioned into energy bands indicative of potassium, thorium, uranium and total count. Height corrected flight line data were obtained for a 28 km square area with the top left hand corner at GR Measurements were obtained using a 30 liter crystal at 50 m intervals along flight lines oriented east-west 80 m above the ground, with 400 m between flight lines. The image obtained with airborne radiometrics differs significantly in several aspects compared with reflectance satellite imagery. The signal with satellite imagery uniformly averages emissions from pixels (picture elements or grid cells) having a well-defined size, with complete independence in measurements between pixels. With radiometric data, the spatial averaging is markedly non-uniform, and there is overlap of areas contributing to signals for adjacent measurements along and between lines. Also, while around 95% of the radiometric signal generally derives from the surface 30 cm of soil, the response is non-uniform with depth. With the data used here, more than 75% of the signal for an individual measurement derives from the surface 20 cm of soil, from an area of around 250 m diameter. The derivation of the radiometric signal from the soil surface does not mean that the underlying material does not affect the measurement. The properties of the surface soil are modified by the underlying material through its effect on processes such as leaching and accumulation of ions, thus the radiometric signal usually reflects properties of the entire soil profile. The measurement can also provide information on underlying geological structures, such as faulting (Davy, 1979). The radiometric signal represents a non-uniform average over a large area, which is readily interpretable for large, uniform areas. However, the averaging creates uncertainties with boundaries between features, both in identifying features, and locating boundaries. These uncertainties can be accentuated by other factors such as a low signal to noise, and effects of the griding algorithms. The RMS error in positioning with grided data is roughly equal to a third the flight line spacing, while individual errors can equal the flight line spacing. Care is therefore required to ensure that ground measurements relate to a defined signal, particularly since most ground observations on soils effectively represent point observations. The spatial patterns discernible in airborne radiometrics depend on the relative scales of measurement and the geomorphic patterns, and the separability of radiometric signals (signatures) for the different features. For the data available for MBTA, field sampling to relate soils to the radiometrics should ideally be stratified according to parent material. However, limitations in the geology maps limit the effectiveness of producing such a stratification prior to field sampling, while the poor access with this study prevented identification of parent material for radiometric patches for the entire area during field sampling. Processing The initial processing methods are described by Tunstall and Marks (1997), but the data were re-analysed to take advantage of improvements in analytical techniques. Following removal of tie lines and overlapping sections of flight lines, and the application of a decorrugation filter, the individual bands were grided at a 100 m cell size using a spline algorithm. The grided data 13

15 were exported to MIPS GIS and converted to byte data. For potassium, uranium and thorium, this only involved truncation of values below one and above 256, but data compression was additionally required for total count. The four channel image was classified into 21 classes using the procedure described for the satellite imagery, but with a lower initial number of classes. Also, the co-occurrence analysis was only applied to pixels located on the boundaries between classes because the marked cohesiveness of radiometric classes masks between class associations. Separate identification of the boundaries between major features using filters (Tunstall and Marks, 1997) was not undertaken here because the boundaries were clearly identified in the classification. Just as different vegetation types can have equivalent spectral signatures, different soils can be associated with equivalent radiometric signatures. As the two prime factors of parent material and weathering affect the radiometric signal, a given signal can arise for different reasons. Thus, even though soils are the product of parent material and alteration through processes such as weathering and eluviation, the provision of an unambiguous soils map requires separation of the effects of these factors. That is, while the radiometrics map patterns of soils, separation of the effects of parent material and weathering is required for interpretation and labelling. The uncertainties due to the patterns of radiometrics arising from the effects of two main factors are often clearly evident because a given radiometric class usually only occurs within a few geological formations. These uncertainties, or ambiguities, can be largely removed by reference to zones based on the geological formations. A zonation of 11 classes prepared for this purpose is given in Fig. 4, where the boundaries of most zones were derived from major boundaries identified in the radiometrics. However, further examination indicated that most ambiguities could be removed using three zones equivalent to those used for vegetation in representing the hills, the Mary River flood plain, and the eastern plains. Field Survey Field survey was conducted on 10 days of the 1995 dry season, and 5 days in Eighty sites were described in the first period and 30 in the second. The dry conditions meant that around half the sites had been burnt to various degrees, while the selection of sites for other purposes meant that 30 sites from the initial survey were not covered by the radiometrics. The measurements obtained at each sample site subdivide into site definition, and variables characterising soil and vegetation. The main site information comprised location, catenary position, slope, aspect, and geology. Locations were recorded using GPS to a maximum error of 50 m. Site selection was based on radiometric patterns, but was severely constrained by access. Initial sampling was at low density along the Jim Jim Road, which cuts through the NE corner of the area, and down rough tracks running NS in the eastern plains. Subsequent sampling provided more detail within localised areas, as well as extending observations to southern parts of the Mary River flood plain, and the southern hills. Vegetation Vegetation data were recorded at each site by establishing a 20 meter square characterisation plot within which the structure and floristics were typical of the surrounding pattern. Thus plots were located subjectively but without pre-conceived bias. The method has been 14

16 described elsewhere (Morgan & Orr 1989, 1992) and was used by the Conservation Commission of the Northern Territory (CCNT) for their territory-wide mapping program (Wilson et al 1990). Vegetation structural classification follows the scheme proposed by Specht (1970). The average height and projected canopy cover were recorded for each vegetation life form present in the plot, and dominant lifeform and projected canopy cover were recorded for each vegetation layer present. A complete floristic list was compiled for each layer, with projected cover recorded for each taxa in each layer. Basal area was estimated for stems of overstory species using a modified Bitterlich wedge (1948). This measurement was found to give a more reliable estimate of species dominance than projected canopy cover, which varies with season and fire intensity in the monsoon tropics. Recent fire history was also recorded, being crucial to the interpretation of the ground layer statistics. Soil Boreholes were dug using hand augers, with samples being obtained from the surface centimeter, and the A2, B1, and B2 horizons. The surface sample is referred to as the A1 horizon, but this was often difficult to discriminate. Horizon boundaries were visually discriminated, with judgements being assisted by the density changes evident with hand augering. Rock samples were obtained whenever possible, either from the borehole, or adjacent to the sample site. The strategy for soil descriptions was to recognise 4 layers or horizons, and to quantify the main variables for each layer using objective continuous, or pseudo-continuous measurements wherever possible. The codes associated with recording and analysis of soil variables are given in Table 3. All records were obtained in the field, including soil chemical analyses. The variables measured partition into physical (thickness, texture, % gravel) and chemical (ph, redox, specific conductivity), and hybrid (colour, peds). Gravel content was determined gravimetrically on sieved samples (2 mm). Texture was determined using the standard field technique, with estimates referenced to a pseudo-continuous measure (Table 3) to facilitate recording and analysis. The thickness of the B2 horizon was seldom measured. Chemical analyses were obtained using electronic meters on 1:5 soil water suspensions, with preparation and analysis being conducted in the field, but with ph additionally being measured using universal indicator solution. A 10-g sieved (0.2 mm) sample was weighed in a sealable plastic bag on an electronic balance, with an allowance being made for the estimated water content, and mixed with 50 ml of distilled water. Soils were thoroughly dispersed, and allowed to settle before measurement. Soil colour was determined by reference to a Munsell colour chart. 15

17 Table 3. Numeric codes used for the description and analysis of soil and landscape properties. CATENARY POSITIONSOIL TEXTURE ClassDescriptorClassQualifierDescriptorSOIL DEPTH 1Pond1coarseGravel Superficial 2Plain, wet2mediumgravel shallow 3Seepage zone3finegravel thin 4Swale, wet4coarsesand mid-deep 5Saddle5fineSand deep 6Incised drainage6loamysand >1.00giant 7Levee7clayeySand 8Plain, dry8 Sandy-loamGRAVEL % 9Lower slope9finesandy-loam < 1 %rare 10Mid-slope10claySandy-loam1-5 %sparse 11Upper slope11fineloam6-20 %common 12Crest12 Loam21-40 %frequent 13Ridge13siltyLoam41-75 %abundant 14sandyClay-loam > 75 %extreme TERRAIN15siltyClay-loam 1Mountainous16 Clay-loamGRAVEL SIZE mm 2Hilly17 Sandy-clay2-7fine 3Undulating18 Silty-clay8-20medium 4Rolling19 Light-clay21-60coarse 5Flat20mediumLight-clay61-100cobble 21 Medium-clay > 100boulder TEXTURE PROFILES22heavyMedium-clay TexB2 TexA2 / ThickB123 Heavy-clay 0 19Uniform24veryHeavy-clay 20 49Gradational Duplex > 100X-duplex COLOURRATINGVALUE CHROMA HUE 7pale, leached2 saturated1bleached (5Y) 0.4olive 6pale-yellow32light (2.5Y) 0.6pale-yellow 5yellow43mid-light (10Yr) 0.8yellow 4brown54mid-dark0.9yellow-brown 3red-brown65 (7.5Yr) 1.0brown 2red76dark (5Yr) 1.2red-brown 1dark red8 leached7 (2.5Yr) 1.4red 8saturated (10R) 1.6red COLOUR RATING = Value Hue (Chroma /10) 16

18 Table 4a. Numeric codes assigned to geological formations WildmanMundogieNorth Mt. South Mary R Mary R GerowieKoolpinKombolgieLateritic Hills Bonnie Hills Plain floodplain Plateau Table 4b.Scheme used to aggregate radiometric classes within geological zones to produce soil groups. Radiometric Soil ClassEastern HillsMary Mt. MundogieKombolgieLateritic Plains River Bonnie Plateau Analysis Vegetation Field data were processed using Microsoft Access, allowing integration with the image processing software. Each site was assigned a particular land cover class on the basis of vegetation structure, overstory species dominance, and understory floristic composition. A library of land cover classes for Northern Australia is currently under compilation at the Military Geographic Information Pilot Project, and is to be compliant with the Australian Defence Force data standard DIGEST Level 2 for analysis at 1:50,000 scale. The Mount Bundey plot data were matched with library vegetation classes for the Top End of the Northern Territory. Plot sites were overlayed on the final land cover classification for the purpose of labelling themes. In cases where more than one land cover class was recorded for a particular theme, 17

19 the choice was made on the basis of class frequency within the theme or, if this was indeterminate, by invoking ER Mapper s Typicality Index for the image data at each plot site. The predicted total area for each land cover class was calculated from the total number of image pixels in each theme, and expressed as a percentage of the total area. The accuracy of the final vegetation classification for MBTA has not been calculated, however the methodology has been tested for accuracy in other parts of Northern Australia and published for Melville Island (Orr & Morgan 1993). That study showed a final accuracy of prediction of 92.8%, and the validity of the MBTA land cover classification is not expected to differ significantly from that figure. Soils Stepwise linear regression was used to determine the significance of relationships between geological formations, radiometric class, catenary position, and soil horizon with soil properties. This was repeated for each horizon separately, and for geological formations having more than 3 samples. The use of regression limited difficulties associated with the large disparities in replication for the different combinations of factors. A given radiometric class is seldom associated with only one geological formation as the signal depends on weathering as well as the parent material. The separation of the effects needed to provide an unambiguous mapping of soils was achieved by identifying major geological zones (Fig. 4), and re-labeling radiometric classes according to zone as indicated in Table 4. Similar classes were aggregated within zones, but the same classes were usually split between zones. The resulting aggregation is referred to as soil groups. Stepwise linear regression was repeated for the soil groups for the main geological categories. An analysis of variance (ANOVA) was also applied to obtain group means, and to provide an indication of the significance of differences. 18

20 RESULTS Vegetation The vegetation of Mount Bundey Training Area can be broadly divided into three biogeographical regions, the Mary River flood plain, the low ranges of metamorphic hills, and the undulating plains and rises of the upper Wildman River and Craig Creek catchments. The Mary River flood plain is predominantly vegetated with mixed woodlands tending to become open in the flatter areas, interspersed with some large areas of grassland. The most widespread assemblage on the plain (4,700 ha) was Eucalyptus polycarpa Woodland, with either E. grandifolia or E. latifolia as co-dominant. Melaleuca nervosum was abundant at most sites surveyed. Clumps of Lophostemon lactifluus were a feature in the lower areas or along minor watercourses. Open grasslands of Eriachne burkitii and E. glauca, often with a co-dominance of Cyperus spp. and other sedges, extended up to 2km in some areas of the flood plain, and were typically interspersed with M. viridiflora Low Open-woodland. Zones of M. viridiflora Low Woodland or Low Open-woodland containing Grevillea pteridifolia and Pandanus spiralis margined the grasslands on slightly elevated levees. Low sand ridges throughout the area support woodlands of E. tetrodonta / E. miniata, while the emergent siltstone rises were characterised by E. latifolia /E. confertiflora. Erythrophleum chlorostachys was a common co-dominant of both these mixed woodlands, and Sorghum intrans the common ground cover. The region of metamorphic siltstone and sandstone hills running north-south through the MBTA support several classes of dry hill woodland typical of this latitude and climate. Eucalyptus tintinnans Woodland or Low Woodland is dominant on the crests and steeper slopes, with E. tectifica commonly occurring on the lower slopes and foothills. Isolated stands of E. dichromophloia were found to occur on some ridges at scales too small to classify, though this species was a common co-dominant with both E. tintinnans and E. tectifica. The grasses Schizachyrium fragile, Heteropogon contortus and Themeda spp. were most common, often with a mantle of annual Sorghum. Where soil depth increased, E. tetrodonta / E. miniata Woodland was often present. Depositional drainage areas within and between the ranges supported vegetation assemblages more typical of the Wildman River catchment to the east. Approximately 65% of the MBTA lies within the catchments of Craig Creek and the West Wildman River. Vegetation patterns here are broadly associated with the erosion and deposition of a series of low rises, rock outcrops, and drainage lines. Various classes of eucalypt woodland prevail throughout the area, with small tracts of Melaleuca viridiflora Low Woodland in poorly drained depositional areas. Low rises with shallow soil depth typically support E. tetrodonta / E. tectifica Woodland over annual Sorghum, the most extensive vegetation class in the MBTA occupying over 25,000 ha. The E. tetrodonta /E. miniata / E. bleeseri assemblage was found to occur more often in the less rocky areas. Non-incised drainage lines with better drained soils supported woodlands of E. polycarpa, E. grandifolia, E. papuana, and E. bigalerita over perennial grasses. In the northern extremity of the MBTA, lateritic rises with deeper sandy or gravelly soils were found to support an Open-forest of E. miniata, E. tetrodonta and Erythrophleum chlorostachys, with Eucalyptus bleeseri on the crests or plateaux. This vegetation assemblage is the most abundant throughout the Top End of the Northern Territory. 19

21 Two other vegetation classes with limited distribution in the MBTA are Melaleuca cajeputi and M. leucadendra Woodland/Open-forest. The former is common on the lower Mary River flood plain to the north of the training area, and throughout the Alligator Rivers region to the east. In the MBTA it occurs only as small isolated patches close to the western boundary, and occupies only 40 ha in total. M. leucadendra is a riparian species, and this vegetation class is associated with major streamlines in the vicinity of the Mary River and Craig Creek. Semi-deciduous monsoon forest has been recorded from a small number of isolated sites within the MBTA, and it is possible that its distribution within the hills has been overestimated in the classification due to the effects of hill shading. It is typically found in sheltered areas on the SW side of rocky ridges. Table 5. Mapped vegetation categories for the Mt Bundey Training Area Cl.DescriptionArea Area Rank (ha) (%) 1Grassland (Themeda, Sorghum, Heteropogon)1, Grassland (Eriachne spp.)2, Eucalypt / Paperbark Woodland (E.polycarpa, E.grandiflora, E.latifolia, M. nervosum) 4, Paperbark Low Open-woodland (M.viridiflora)2, Paperbark Low Woodland (E.latifolia, M.viridiflora)2, Low Open-woodland (Grevillea, Pandanus, M.viridiflora)1, Eucalypt Woodland (E.latifolia, E.confertiflora)2, Paperbark Woodland/ Open-forest (M.cajeputi) Paperbark Woodland/ Open-forest (M.leucadendra) Eucalypt Woodland (E.papuana, E.bigalerita, E.polycarpa)3, Eucalypt Woodland (E.grandifolia, E.latifolia, E.confertiflora)1, Eucalypt Woodland (E.tetrodonta, E.tectifica)25, Eucalypt Woodland (E.tectifica, E.dichromophloia, E.clavigera)7, Eucalypt Open-forest (E.tetrodonta, E.miniata, E.porrecta)16, Eucalypt Woodland (E.tetrodonta, E.miniata, E.confertiflora)11, Eucalypt Low Woodland (M.viridiflora, E.polycarpa)3, Eucalypt Open-forest (E.tetrodonta, E.miniata, E.bleeseri)5, Eucalypt Woodland/ Low Woodland (E.tintinnans, E.dichromophloia, E.confertiflora) 19Eucalypt Woodland (E.dichromophloia, E.miniata, E. tetrodonta, E.tectifica) 7, , Monsoon Forest/ Open-forest (semi-deciduous species and vines)1, Eucalypt Low Woodland (E.foelscheana, E.grandifolia)1,

22 Table 6. Descriptions of soil properties for the mapped soil groups. Results in italics were not included in the statistical analysis. Geology (replicates)phchromahuethicknesstexturetexture ChangeGravel B2B2A2A2A2 - B2/ThickB1A1 1WildmanDry plainaciddarkbrownthinfine loamgradationalsoilabundantgravel 2 (blocky phase)mid-slopeneutralmid-darkyellow-brownsuperficialloamx-duplexsoilfrequentgravel 3 Wet plainneutraldarkbrowngiantsilty clay-loamuniformsoilraregravel 4 Dry plainaciddarkbrownthinsilty loamuniformsoilcommongravel 5 CrestAcidDarkRed-brownThinSilty clay-loamgradationalsoilabundantgravel 7 CrestAcidDarkBrownThinClay sandy-loamduplexsoilfrequentgravel 8WildmanRidgeNeutralMid-darkRedShallowLoamy sanduniformsoilabundantgravel 9 (shale phase)dry plainacidsaturatedyellow-brownthinclay-loamgradationalsoilfrequentgravel 10 SwaleAlkalineMid-lightOliveMid-deepSilty clay-loamgradationalsoilraregravel 11 Lower slopeneutraldarkyellow-brownmid-deeploamuniformsoilfrequentgravel 12 Lower slopeneutralmid-darkyellowshallowloamy sanduniformsoilraregravel 14MundogieWet plain (2)AcidDarkYellow-brownShallowSilty-clayGradationalsoilFrequentgravel 15 Upper slope (4)AcidSaturatedRed brownshallowsilty-clayuniformsoilabundantgravel 16GerowieLower slopeacidmid-darkyellow-brownshallowsandy-clayduplexsoilextremegravel 18 Lower slopeaciddarkbrownshallowsandy-claygradationalsoilabundantgravel 20 SwaleAcidDarkYellow-brownThinSilty clay-loamgradationalsoilabundantgravel 22 CrestAcidSaturatedBrownShallowClay sandy-loamduplexsoilabundantgravel 23KoolpinWet plainaciddarkbrownshallowclay sandy-loam?soilfrequentgravel 24 Upper slopeaciddarkbrownshallowclay sandy-loamgradationalsoilfrequentgravel 25Mary River FloodWet plainneutralmid-darkyellowshallowclay sandy-loamduplexsoilraregravel 27Plain propercrestaciddarkyellow-brownshallowsilty loamduplexsoilfrequentgravel 29Mary River PlainWet plainneutralmid-darkyellowthinclayey sanduniformsoilcommongravel 30 (sediments fromwet plainneutrallightyellowmid-deepsilty loamgradationalsoilraregravel 31adjoining hills)swaleneutralmid-darkbrownshallowclay sandy-loamuniformsoilcommongravel 32Mt BonnieRidge (4)AcidBleachedPale yellowthinfine loamuniformsoilextremegravel Swale, wet(2)acidmid-lightpale yellowshallowfine loamgradationalsoilraregravel 33KombolgieLower slope (1)High aciddarkbrownshallowsilty clay-loamduplexsoilabundantgravel 34Lateritic PlateauDry plain (1)NeutralDarkYellowShallowFine loamgradationalsoilabundantgravel 21

23 Soils The 21-class map derived from the radiometrics is given in Fig. 6. The pinks and purples are associated with low radiometric emissions, while the browns are generally associated with boundaries between major features. The greens can be associated with the blocky phase of the Wildman Siltstone, Gerowie Tuff, and the Mary River flood plain proper. Class 1 is effectively only associated with the blocky phase of the Wildman Siltstone. In the eastern plains the purple colours generally identify a shale phase of the Wildman Siltstone, but they are also associated with Mundogie Sandstone. Because of the low signal level for this category, the transition from hills to drainage lines (light pink) is gradational, and often poorly defined. Conversely, the transition between the relatively high signal levels for the blocky phase of the Wildman Siltstone and the major drainage lines is sharp. In the hills, the Koolpin Formation is clearly discriminated from the Gerowie Tuff, and the Wildman Siltstone is reasonably distinct, but the small occurrences of other formations are not clearly discriminated. Significant results from stepwise linear regression of relationships between soil properties and radiometric class, geological formation, catenary position, and profile horizon are given in Table 1 in Appendix 2. Significance levels are higher when the analysis includes all horizons, and the main results for combined horizons are: All variables exhibit significant relationships. Radiometric class, horizon, catenary position, and geology are most significant. Horizon effects are most significant for soil texture, thickness, and colour. ry position is most significant for gravel content. Radiometric class and geology are most significant for soil chemical properties, but catenary effects are prominent. The main results for horizons analysed separately are: ry position only is significant for physical properties, thickness excepted. Geology and radiometric class only are significant for soil chemical properties, and soil thickness. Geology and radiometric class are most significant for soil colour, but catenary effects also occur. Soil chemical properties are most strongly related to parent material as reflected by geology and radiometric class, while soil physical properties such as texture and gravel content are most strongly related to position in the landscape. There is little difference between the level of discrimination provided by geology and radiometric class, and interactions between geology and radiometric-class are significant for all chemical measures with horizons combined. These interactions evidence the ambiguities arising in the interpretation of radiometric signal due to differences in parent material. The re-allocation of radiometric class according to geology as indicated in Table 4 was undertaken to address the interactions between radiometric class and geology (Table 1, Appendix 2). The results for analyses for these groups (Table 2, Appendix 2) indicate that the 22

24 re-allocation was successful in removing ambiguities arising from equivalent radiometric signals deriving from different materials. The main results for combined horizons are: Soil group effects are significant for all properties in the ANOVA. Horizon effects are generally significant with both analyses. effects are generally significant with the regression analysis. The main results for separate horizons are: Only group effects are significant with the ANOVA ry effects are dominant in the regression analysis. The main features of these results are the high significance of the group effects, and the large decrease in significance of geological and radiometric effects. The stepwise regressions of the soil group data provide similar results to the ANOVA, but assign greater significance to catenary position, and provide higher discrimination because of the highly unbalanced design. The mean values and standard errors for the soil groups derived from the ANOVA are given in Tables 3 and 4, Appendix 2. These indicate the characteristics of the classes by way of levels and variability, and therefore provide a basis for labeling the mapped classes, and for evaluating the reliability of the mapping. Labels for soil groups for which field samples were obtained are given in Table 6. The map of soil groups derived by aggregating radiometric classes within geological zones is given in Fig. 7. Of the 34 soil groups, two represent very minor occurrences. The use of averages in generating the profile descriptions in Figure 6 masks the range of profile characteristics. A second table of profile descriptions was therefore produced to identify the range of profile types associated with the geological categories (Table 7). With this form of presentation the mapped geological categories serve to define soil landscapes (Fig. 7), and the descriptions in Figure 7 the profile characteristics for catenary positions within the soil landscapes. 23

25 Table 7. Descriptions of soil profile properties for catenary positions for the mapped soil groups. Geology (replicates) phchromahuethicknesstexturetexture ChangeGravel B2B2A2A2A2 - B2/ThickB1A1 WildmanWet plainneutraldarkbrowngiantsilty clay-loamuniformsoilraregravel (blocky phase)dry plainaciddarkbrownthinsilty loamuniformsoilcommongravel Mid-slopeNeutralMid-darkYellow-brownSuperficialLoamX-duplexsoilFrequentgravel Upper slopeaciddarkred-brownthinsilty clay-loamgradationalsoilabundantgravel CrestAcidDarkBrownThinClay sandy-loamduplexsoilfrequentgravel WildmanWet plainalkalinelightolivemid-deepmedium light-claygradationalsoilraregravel (shale phase)swalealkalinemid-lightolivemid-deepsilty clay-loamgradationalsoilraregravel Lower slopeneutralmid-darkyellowshallowloamy sanduniformsoilraregravel Lower slopeneutraldarkyellow-brownmid-deeploamuniformsoilfrequentgravel Dry plainacidsaturatedyellow-brownthinclay-loamgradationalsoilfrequentgravel Mid slopeacidlightbrownthinfine sandy-loamuniformsoilfrequentgravel Upper slopealkalinedarkbrownthinclay sandy-loamduplexsoilabundantgravel Upper slopeneutraldarkbrownthinloamy sandgradationalsoilfrequentgravel RidgeNeutralMid-darkRedShallowLoamy sanduniformsoilabundantgravel MundogieWet plainaciddarkyellow-brownshallowsilty-claygradationalsoilfrequentgravel Upper slopeacidsaturatedred brownshallowsilty-clayuniformsoilabundantgravel GerowieWet plainneutrallightyellowshallowloamuniformsoilsparsegravel Wet plainacidmid-darkyellowshallowmedium-claygradationalsoilcommongravel SwaleAcidDarkYellow-brownThinMedium light-claygradationalsoilabundantgravel Lower slopeacidmid-lightbrownshallowsandy clay-loamduplexsoilraregravel Lower slopeaciddarkbrownthinmedium-clayduplexsoilabundantgravel Dry plainacidmid-darkyellowshallowsandy-claygradationalsoilfrequentgravel CrestAcidMid-darkBrownThinMedium-clayDuplexsoilExtremegravel CrestAcidSaturatedPale yellowshallowclayey sanduniformsoilraregravel KoolpinSwaleAcidDarkRed-brownMid-deepMedium-clayGradationalsoilAbundantgravel LeveeAcidMid-darkYellowShallowSandy-clayGradationalsoilAbundantgravel Lower slopeaciddarkpale-yellowthinlight-claygradationalsoilabundantgravel Mid-slopeNeutralMid-lightYellowThinClay-loamDuplexsoilAbundantgravel Mid-slopeNeutralSaturatedRedShallowSilty-clayGradationalsoilCommongravel CrestAcidDarkYellowThinMedium-clayGradationalsoilFrequentgravel RidgeNeutralSaturatedBrownShallowSilty-clayDuplexsoilAbundantgravel 24

26 Table 7. (cont) Geology (replicates) phchromahuethicknesstexturetexture ChangeGravel B2B2A2A2A2 - B2/ThickB1A1 Mary River FloodWet plainneutralmid-darkyellowshallowclayey sanduniformsoilcommongravel Plain properwet plainacidmid-lightbrownshallowsandy clay-loamduplexsoilnogravel Wet plainneutrallightpale-yellowshallowsandy-clayduplexsoilnogravel SeepageAcidDarkYellowShallowHeavy-clayDuplexsoilNogravel SwaleAcidSaturatedYellowShallowSilty-clayGradationalsoilCommongravel Lower slopeaciddarkyellowshallowlight-claygradationalsoilcommongravel CrestAcidSaturatedBrownThinMedium-clayGradationalsoilAbundantgravel Mary River PlainWet plainneutralmid-darkyellowthinclayey sanduniformsoilcommongravel (sediments fromwet plainneutrallightyellowmid-deepsilty loamgradationalsoilraregravel adjoining hills)swaleneutralmid-darkbrownshallowclay sandy-loamuniformsoilcommongravel Mt BonnieRidgeAcidBleachedPale yellowthinfine loamuniformsoilextremegravel Swale, wetacidmid-lightpale yellowshallowfine loamgradationalsoilraregravel KombolgieLower slopehigh aciddarkbrownshallowsilty clay-loamduplexsoilabundantgravel Lateritic PlateauDry plainneutraldarkyellowshallowfine loamgradationalsoilabundantgravel 25

27 DISCUSSION The maps presented here provide higher discrimination of patterns of interest than previously possible. While this detail is needed, it also means that their usefulness is not immediately apparent, calling for further analysis. The maps provide detailed base information that can be queried to extract information required to meet specific objectives. The detail provided in the vegetation map exceeds that previously available and it is excessive for most applications, conservation excepted. Reinterpretation of the vegetation categories is required for specific purposes such as suitability for parachute drop zones. Mapping in this case is achieved through knowledge of the structural characteristics of the vegetation classes, thereby allowing re-labeling of classes according to the new criteria. The soil group map has been derived to meet the requirement for purpose-specific maps of soil characteristics through: identifying soil properties associated with soil groups, and deriving mapped soil information independently of other information such as terrain. Maps of soil texture, soil depth, and ph can be readily derived, while terrain attributes can be used to address issues such as quarry site selection and trafficability (Tunstall and Marks, 1997). While map detail depends in part on the effort expended, the broad categorisation of soils in Fig. 2 illustrates the difficulties in mapping soils in MBTA from landscape alone. Much more effort was expended in producing the geology map (Fig. 1) than the soils map (Fig. 2), resulting, for the eastern plains, in much greater detail of landscape-related patterns for geology than soils. The geology map could therefore have been used as the basis for mapping soils for much of MBTA, but even this fails to map patterns within the Mary River flood plain. The results of Tunstall and Gourlay (1994) for the Singleton Training Area (STA) showed that the soil landscape approach to mapping failed to identify some significant patterns of soils, even when detailed and accurate information on geological formations was taken into account. Large differences in materials can occur within formations, as with the Wildman Formation here, reflecting in soil properties but not in landscape patterns. At STA, the effects of parent materials on soil properties were around five times more significant than catenary position, and catenary effects were only significant when parent material was accounted for. Therefore, failure to identify patterns of parent material can greatly reduce the significance and reliability of results. Results here show that radiometrics can identify parent material and discriminate patterns of soil properties, improving the significance and reliability of mapping. However for most physical soil properties, landscape or catenary effects were more significant here than at STA. Apparently, the long transport distances for material in the highly erosive environment of the Northern Territory diminish the effects of parent material through mixing and leaching, and enhance catenary effects through erosion, sorting, and eluviation. However, radiometrics still provide the best discrimination of soil chemical properties, and are therefore needed to identify soil landscape patterns relevant to soil properties. The soil categories mapped here are well defined, and statistically discriminated, but errors in mapping and description arise from limitations in the radiometric data, and the circumscribed field sampling. However, the evident errors do not negate the results one result of 26

28 providing detail in maps is the ease with which exceptions or errors can be detected. Nevertheless, it emphasises the significance of scale dependence in such mapping, and contrasts with the difficulty of evaluating reliability with soil landscape mapping. Statistical analysis indicated that the soil groups identify the main patterns associated with parent material, but detailed examination of the radiometric mapping and field data indicated that the information on landscape-related patterns of soils in the radiometric data is incomplete. This lack derives from the effective spatial and spectral resolutions of the data. The minimum effective pixel size is greater than 1 ha, and this prevents discrimination of fine patterns, as occur in drainage systems in the hills, while the low signal-to-noise ratio limits discrimination of drainage systems from the shale phase of the Wildman Siltstone in the eastern plains. In summary, while additional field sampling is highly desirable, with the available data there is a limit to the possible resolution in mapping soil patterns. Two approaches to improving map reliability are (1) to obtain higher resolution radiometric data, or (2) to integrate the existing radiometric information with an analysis of terrain. The main radiometric blocks in Fig. 7 could be used to define soil landscapes, with soil units being mapped within landscapes using terrain patterns. While such terrain analysis would be effective in the hills, it would be ineffective on the plains because of the low gradients and the 10-m resolution of the elevation data. Also, the incorporation of terrain would limit the use of any map in modelling specific applications, such as trafficability assessment. Application of higher resolution radiometrics would require further expenditure on data collection, but so too would application of terrain analysis, as the elevation data needed must have much higher resolution than is currently available. Improved understanding of the way in which terrain and parent material determine soil properties is also needed (these aspects were not clearly identifiable in the field, nor were they fully seen in the analysis of field data). The approach to soil mapping used here differs from that generally applied to large areas, in that it focuses on parent material rather than catenary position, on properties rather than soil types, and in its statistical testing of the results. The emphasis on soil properties rather than on soil types allows direct application of the results (without precluding the derivation of soil types), and so is unquestionably beneficial. The measurement of gravel content provides a good example. Gravel is common in most soils, up to 80%, which is significant for vegetation and military activities. A gravel-free soil with a phosphorus concentration of 2 ppm contains as much phosphorus as a soil with 10 ppm P but 80% gravel, hence the absence of gravel content in a soil description limits the value of any chemical analysis. The abundance and type of gravel affect evaluations for specific applications, such as trafficability and road construction (Tunstall and Marks, 1997). The focus here on parent material arises partially for the same reason that soil mapping has previously focussed on landscape the availability of information. Soils are a product of parent material and its alteration through weathering, transport, eluviation, but the information on parent material was usually limited to that given in 1:250,000 geology maps, where areas of most commercial interest are mapped as Quaternary alluvium or Cainozoic deposits. These categories do not identify the nature of the material, and so useful information on parent material has seldom been available to those mapping soils. The radiometric data provide information on the spatial distribution of materials, and therefore make possible the use of a factor previously known to be significant in determining patterns of 27

29 soils. The statistics presented here and by Tunstall and Gourlay (1994) demonstrate the significance of this development. Developments The soil information presented here does not cover all of MBTA, and omits the area of the Mary River flood plain most impacted by military activities to date. We therefore recommend that radiometric data should be acquired for the remaining areas to allow completion of the soil map for the training area. Given the flat terrain of the areas yet to be covered, the only alternative is to map patterns of soils using vegetation patterns, but best results would be obtained by acquiring high resolution radiometric data (60 m height, 200 m flight line spacing). This would provide significantly greater discrimination than provided here, particularly given recent developments in processing 256-channel radiometric data. Apart from providing information to support military land use, the joint mapping of vegetation and soils on MBTA was undertaken in an attempt to integrate the mapping and analysis of vegetation and soils. Despite the acquisition of detailed site descriptions for soils and vegetation, this was not achieved. The soil and vegetation maps can be compared to determine associations, but the site data do not allow structured statistical analysis of factors determining the distribution of vegetation, as was produced for the soils. While the vegetation was described using structural criteria, which were represented as continuous variables, it appears that analysis in the form desired is most readily achieved using different vegetation attributes. This different form of description will significantly change the representation of vegetation, and hence involve a change in perception. It may also result in a decrease in the discrimination of vegetation types, at least initially, but is worth pursuing if it offers the prospect of routine analysis of factors determining the distribution of vegetation. The requirements for natural resource information for the new Bradshaw Field Training Area (BFTA) are the same as for MBTA, but the large size of MBTA makes mapping soils difficult using traditional methods, particularly given the terrain. However, it is imperative that reliable mapped information be available because of the potential for impact by vehicles, and consequent erosion. As high quality radiometric data exist for the main manoeuvre area, the minimum requirement is that these be used to map patterns of soils relevant to military activities, including road construction. It is desirable that radiometric data be obtained for the remainder of the area subject to impact by military activities. 28

30 REFERENCES Anderson, R. and Tunstall, B. R. (1992) Mount Bundey Training Area 1992 Field Survey Consultancy report to Defence by Kinhill Engineers and CSIRO Division of Water Resources. Bitterlich, W Die winkelzahlprobe. Allg. Forst- u. Holzwirtsch. Ztg., 59, 1/2: 4-5. Christian, C. S. and Stewart, E. A. (1968). Methodology of integrated surveys. In: Aerial surveys and integrated studies. Proc. of the Toulouse Conference, UNESCO Paris. pp Dunlop, C.R. (ed) Checklist of the vascular plants of the Northern Territory, Australia. N.T. Parks and Wildlife Commission. Davy, R. (1979). A study of lateritic profiles in relation to bedrock in the Darling Range near Perth, W.A. Geological Survey of W.A. Report 8. Gourlay R. C., Orr, T. M., Marks, A. S. and Tunstall, B. R (1996). Provision of natural resource information for military land use and management. In Environmentally Responsible Defence, Ed. P. Crabb, J. Kesby, L. Olive., Australian Defence Studies Centre, Canberra, Aust. pp 107, 124 Kinhill (1991). Mount Bundey Training Area: Draft Environmental Impact Statement. Kinhill Engineers Pty Ltd., Adelaide McDonald, R. C., Isbell, R. F., Speight, J. G., Walker, J. and Hopkins, M. S. (1984). Australian soil and land survey. Field Handbook. Inkata, Melbourne. 160 pp. Morgan, G. A. and Orr, T. M Military applications of remote sensing for terrain intelligence. Institution of Engineers, Australia. Fifth National Engineering Symposium, Canberra. Morgan, G. A. and Orr, T. M Vegetation and terrain analysis for military planningremote sensing as a practical tool. In Proc. Sixth Australian Remote Sensing Conference, Wellington, NZ. Northcote, K. H. (1979). A factual key for the recognition of Australian soils. CSIRO. Rellim, Adelaide. 124 pp. Specht, R. L Descriptions of plant formations. In G.W. Leeper (ed) The Australian Environment. Melbourne University Press. Stace, H. C. T, Hubble, G. D., Brewer, R., Northcote, K. H., Sleeman, J. R., Mulcahy, M. J., Hallsworth, E. G. (1968). A Handbook of Australian Soils. Rellim, Adelaide. Story, R., Galloway, R. W., McAlpine, J. R., Aldrick, J. M. and Williams, M. A. J. (1976). Lands of the Alligator Rivers Area, Northern Territory. CSIRO, Australia, Land Research Series No. 38, 171 pp. Story, R., Williams, M. A. J., Hooper, A. D. L., O'Ferrall, R. E. and McAlpine, J. R. (1969). Lands of the Adelaide-Alligator Area, Northern Territory. CSIRO, Australia, Land Research Series No. 25, 154 pp. Tunstall, B. R. (1996) Integration of military training and conservation in the Shoalwater Bay Training Area. In Environmentally Responsible Defence, Ed. P. Crabb, J. Kesby, L. Olive., Australian Defence Studies Centre, Canberra, Aust. pp. 203,

31 Tunstall, B. R. and Gourlay, R. C. (1994). Soil surveys conducted on the Singleton Training Area. CSIRO Australia Division of Water Resources and Environmental Research Information Consortium, Consultancy Report. Tunstall, B. R., Jupp, D. L. B. and Mayo, K. K. (1984). The use of co-occurrence in land cover classification for the investigation of ecological landscape patterns. In Landsat 84, Proc. 3rd Australasian Remote Sensing Conf., Gold Coast, Queensland, (Organising Committee, Brisbane). Tunstall, B. R., Harrison, B. A. and Jupp, D. L. B. (1987). Incorporation of geographical data in the analysis of Landsat imagery for land use mapping a case example. Proc. 4th Australasian Conf., Adelaide. Tunstall, B. R., Orr, T. M., and Marks, A. S. (1998). Soil and Vegetation Mapping: Mt. Bundey Training Area. CSIRO Aust. Land and Water. Technical Report 8/98. Wilson, B. A., Dunlop, C.R., Brocklehurst, P. S., Clark, M. J. and Barritt, M. J The vegetation of an area of Mt Bundey Station proposed training area for 2nd Cavalry Regiment. Technical Memorandum 89/6. Conservation Commission of the Northern Territory. Wilson, B. A., Brocklehurst, P. S., Clark, M. J. and Dickinson, K. J. M Vegetation Survey of the Northern Territory. Conservation Commission of the Northern Territory, Darwin. Technical Report No

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