The analysis employed Google Earth Engine's cloud computing infrastructure to process ,[object Object], and ,[object Object], across the 34,208 km² study area. This computational approach enables consistent analysis across the extensive study area while maintaining the spatial resolution necessary for meaningful terrain characterization. The methodology follows established geospatial engineering standards used in infrastructure planning globally.

[object Object], Terrain slope was derived using Horn's algorithm applied to the SRTM digital elevation model within a 3×3 pixel neighborhood. The mathematical expression for slope gradient is:

Where ,[object Object], and ,[object Object], represent the rate of elevation change in the east-west and north-south directions respectively. This algorithm provides robust slope estimates by considering elevation values in all eight surrounding pixels, reducing sensitivity to noise in the underlying elevation data.

[object Object], Vegetation density was quantified using the Normalized Difference Vegetation Index:

Where B8 represents Sentinel-2's near-infrared band (842nm) and B4 represents the red band (665nm). Annual median compositing across ,[object Object], minimized atmospheric contamination and provided robust estimates of typical vegetation conditions. NDVI values range from -1 to +1, with healthy vegetation producing values above 0.3 and bare soil/rock typically falling below 0.1.

[object Object], Soil moisture variability was assessed through Sentinel-1 VV-polarized backscatter coefficients. The VV polarization (vertical transmit, vertical receive) is particularly sensitive to soil moisture conditions in sparse vegetation environments like the Pilbara. Higher backscatter values correlate with increased surface roughness and moisture content, while temporal stability indicates consistent ground conditions suitable for road construction. The seasonal comparison approach—contrasting wet season (January-February) with dry season (June-August) acquisitions—provides a direct measure of soil moisture variability relevant to road construction scheduling.

# SRTM Digital Elevation Model - 30m resolution
dem = ee.Image("USGS/SRTMGL1_003")
dem_clipped = dem.clip(pilbara_aoi)

# Calculate terrain metrics using Horn's algorithm
elevation = dem_clipped.select('elevation')
slope = ee.Terrain.slope(dem_clipped)  # Output in degrees
aspect = ee.Terrain.aspect(dem_clipped)
hillshade = ee.Terrain.hillshade(dem_clipped)

# Define slope stability classes for road construction
stable = slope.lt(5)                              # <5°: Ideal
mod_stable = slope.gte(5).And(slope.lt(15))       # 5-15°: Minor grading
marginal = slope.gte(15).And(slope.lt(25))        # 15-25°: Significant earthwork
unstable = slope.gte(25).And(slope.lt(35))        # 25-35°: Major engineering
very_unstable = slope.gte(35)                     # >35°: Avoid

# Calculate area for each class
pixel_area = ee.Image.pixelArea()
stable_area = stable.multiply(pixel_area).reduceRegion(
    reducer=ee.Reducer.sum(),
    geometry=pilbara_aoi,
    scale=100,
    maxPixels=1e9
).getInfo()

def add_ndvi(image):
    ndvi = image.normalizedDifference(['B8', 'B4']).rename('NDVI')
    return image.addBands(ndvi)
s2_ndvi = s2.map(add_ndvi)
ndvi_median = s2_ndvi.select('NDVI').median().clip(pilbara_aoi)

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Elevation Metric	Value	Engineering Implication	Source
Minimum Elevation	287 meters	Valley floor reference for drainage design	SRTM DEM
Mean Elevation	607.6 meters	Representative working elevation	SRTM DEM
Maximum Elevation	1,244 meters	Ridge crest avoiding steep terrain	SRTM DEM
Standard Deviation	146.3 meters	Moderate relief variability	SRTM DEM
Figure 1: Digital Elevation Model visualization showing the Pilbara study area. Color gradient from green (lower elevations around 287m) through yellow and orange to brown/white (higher elevations up to 1,244m). The relatively uniform coloring across large areas indicates the plateau-dominated terrain favorable for road construction. Note the linear darker features indicating incised drainage channels cutting through the plateau surface—these represent the primary crossing challenges for road alignments.
Figure 2: Elevation distribution profile across the study area, illustrating the statistical spread of terrain heights. The concentration of values around the 600m mean confirms the plateau-dominated landscape with a clear central tendency.
Figure 3: Hillshade relief visualization providing three-dimensional context for terrain features. This shaded relief map, generated by simulating illumination from the northwest, highlights the mesa-and-valley topography characteristic of the Hamersley Range. The bright (sunlit) and dark (shadowed) contrasts emphasize slope breaks and escarpments that would require special attention in road alignment planning.

The ,[object Object], (maximum minus minimum) reflects the region's characteristic mesa-and-valley topography, where iron-rich plateau surfaces are dissected by ancient drainage networks. For road planning purposes, this elevation profile presents both opportunities and constraints that must be carefully balanced in alignment selection.

[object Object], The mean elevation of ,[object Object], places most of the region within a single broad elevation band, minimizing the need for extensive switchback alignments that would be required in more mountainous terrain. Plateau surfaces offer extensive areas of gentle gradients suitable for straight-line road corridors. The relatively uniform elevation across large portions of the study area means that many route alternatives exist for connecting any two points—providing flexibility during detailed design to optimize for factors such as drainage, vegetation avoidance, or integration with existing infrastructure. Additionally, the elevated plateau position above many drainage lines means that flooding risk is naturally reduced for ridge-following alignments.

[object Object], The ,[object Object], indicates sufficient relief variability to require careful route selection. Transitions between plateau surfaces and valley floors will require engineered grades, and the highest terrain (approaching ,[object Object],) should be avoided unless operationally necessary. The geological boundaries between the iron-rich Hamersley Group formations and surrounding rock types often correspond to escarpments where slopes steepen—these transition zones require particular attention during route planning. The ancient drainage channels cutting through the plateau create deeply incised valleys that present crossing challenges requiring bridge or culvert infrastructure.

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Stability Class	Slope Range	Area (km²)	Percentage	Road Engineering Requirement
Stable	<5°	24,959.43	72.9%	Ideal—minimal grading required
Moderately Stable	5-15°	6,736.82	19.7%	Suitable—minor cut/fill operations
Marginal	15-25°	1,938.03	5.7%	Challenging—significant earthworks
Unstable	25-35°	408.38	1.2%	Difficult—major engineering, retaining walls
Very Unstable	>35°	33.32	0.1%	Avoid—extreme measures required
Percentile	Slope Value	Interpretation
---------------	----------------	-------------------
5th percentile	0.46°	Flattest 5% essentially level
25th percentile	1.06°	Quarter of area <1° slope
Median (50th)	2.09°	Half of area <2.1° slope
75th percentile	5.37°	Three-quarters under 5.4°
95th percentile	17.37°	Only 5% exceeds 17°
Figure 4: Terrain slope visualization showing gradients across the study area. Green represents stable terrain (<5°), yellow indicates moderately stable zones (5-15°), orange shows marginal terrain (15-25°), and red/dark red highlights unstable to very unstable areas (>25°). The predominance of green coloring visually confirms the 72.9% stable terrain finding. The linear red/orange features following drainage channels illustrate how steep terrain concentrates along valley walls rather than on plateau surfaces.
Figure 5: Pie chart distribution of slope stability classes. The dominant green segment (Stable, 72.9%) and yellow segment (Moderately Stable, 19.7%) together comprise 92.7% of the study area—terrain suitable for road construction without extraordinary engineering measures. The small red and dark red segments (Unstable and Very Unstable, combined 1.3%) represent areas that should be avoided if possible.
Figure 6: Spatial distribution of slope stability classes across the Pilbara study area. This classified map enables identification of continuous corridors through stable terrain (green) while highlighting zones requiring avoidance or special treatment (orange/red). Note how the stable terrain forms extensive interconnected areas, while challenging terrain concentrates in specific zones associated with drainage channel walls and mesa escarpments.
Figure 7: Terrain aspect visualization showing the directional orientation of slopes. Aspect influences sun exposure, which affects pavement durability and dust generation. North-facing slopes (red) receive maximum solar radiation in the southern hemisphere, while south-facing slopes (cyan) receive less. This information supports detailed road design decisions regarding pavement specifications and maintenance requirements.

[object Object], ,[object Object], ,[object Object], falls within the stable or moderately stable categories, representing terrain where standard road construction practices can be employed without extraordinary engineering measures.

The dominance of gentle terrain is further confirmed by slope percentile analysis, which reveals the distribution of slope values across the entire study area:

The ,[object Object], is particularly significant—it confirms that ,[object Object],. Road engineers selecting corridors through median-slope terrain will encounter gradients requiring virtually no special treatment. To put this in perspective, a 2° slope represents a rise of approximately 3.5 meters over 100 meters of horizontal distance—barely perceptible to a driver and well within comfortable gradients for heavy vehicles. The ,[object Object], being higher than the median indicates that the distribution is right-skewed, with a small proportion of steep terrain pulling the average upward.

1. Primary Corridors should traverse the 24,959 km² stable zone, maintaining gradients below 5° throughout. This maximizes construction efficiency, minimizes maintenance requirements, and optimizes fuel efficiency for heavy vehicles. The extensive stable terrain provides numerous alternative alignments, enabling route selection to also optimize for other factors such as drainage crossing minimization, vegetation avoidance, and cultural heritage site avoidance.
2. Secondary/Alternative Routes may utilize the 6,737 km² moderately stable zone where operational necessity dictates. Cut-and-fill operations in 5-15° terrain are routine for Pilbara road construction and do not represent significant cost escalations. The moderately stable zone often provides the shortest-distance routes between points, potentially offsetting additional construction costs with reduced road length.
3. Avoidance Zones comprising the 2,380 km² of marginal to very unstable terrain (slopes >15°) should be excluded from alignment consideration except where absolutely unavoidable. When crossing is required, switchback designs, retaining structures, and enhanced drainage become mandatory, substantially increasing both construction costs and ongoing maintenance requirements.

[object Object], ,[object Object], consists of bare soil or sparse vegetation (predominantly spinifex grassland and isolated mulga scrub). This dramatically reduces vegetation clearance costs, environmental approval complexity, and rehabilitation requirements compared to road construction in forested or agricultural landscapes. The dominant vegetation type—spinifex (Triodia species)—consists of tussock grasses that can simply be driven over during construction without significant clearing operations.

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Statistic	Value	Interpretation
Mean NDVI	0.16	Sparse vegetation typical of Pilbara
Median NDVI	0.15	Consistent sparse cover
10th percentile	0.098	Near-bare conditions
90th percentile	0.22	Upper limit still sparse
Maximum NDVI	0.86	Isolated riparian pockets
Standard Deviation	0.052	Low variability confirms consistent sparse cover
Figure 8: Normalized Difference Vegetation Index (NDVI) visualization. Brown tones indicate bare soil and rock, yellow represents sparse vegetation (the dominant land cover), light green shows moderate vegetation, and dark green highlights the rare dense vegetation pockets. The prevalence of yellow/brown confirms the 90% sparse/bare finding. Note how the linear green features follow drainage channels—these riparian corridors represent the 1% dense vegetation requiring careful environmental assessment if road crossings are necessary.
Figure 9: True-color Sentinel-2 composite showing the characteristic red-brown iron-rich soils and sparse vegetation of the Pilbara landscape. This image provides visual context for the NDVI analysis—the limited green areas correspond to the 1% dense vegetation classification. The distinctive red-brown coloring results from the high iron oxide content of Pilbara soils—the same iron that makes the region one of the world's premier iron ore provinces.

• Enhanced environmental assessment requirements under Western Australian clearing regulations administered by the Department of Water and Environmental Regulation
• Presence of culturally significant vegetation requiring Indigenous heritage consultation under the Aboriginal Heritage Act
• Co-location with drainage features requiring culvert or bridge infrastructure
• Potential habitat for protected species (e.g., Pilbara olive python, northern quoll, ghost bat)
• Increased clearing and rehabilitation costs compared to sparse vegetation zones The correlation between dense vegetation and drainage networks is visible in both the NDVI map and drainage analysis outputs—green corridors follow the blue flow accumulation patterns, confirming this relationship. This correlation actually simplifies planning: by avoiding major drainage crossings (for hydrological reasons), road alignments simultaneously avoid the vegetation zones most likely to trigger complex environmental approval processes.

The ,[object Object], in VV-polarized backscatter is ,[object Object],. For context:

• Seasonal variations exceeding 3 dB would indicate significant moisture fluctuation requiring wet-season road closures and specialized construction scheduling
• Variations of 1-3 dB suggest moderate seasonal effects manageable through appropriate design specifications
• Variations below 1 dB indicate stable ground conditions year-round—the Pilbara clearly falls in this category This stability reflects the region's low annual rainfall (
  ), porous rocky soils that drain rapidly following rainfall events, and the intermittent nature of cyclonic rainfall events that produce short-duration flooding rather than prolonged saturation. The iron-rich Pilbara soils, derived from the weathering of ancient banded iron formations, have excellent drainage characteristics that contribute to the observed ground stability.
  
  Figure 10: Sentinel-1 SAR backscatter (VV polarization) visualization. Brighter areas indicate higher backscatter (rougher surfaces, potential moisture concentration), while darker areas show lower backscatter (smoother, drier surfaces). Linear bright features correspond to drainage channels where water concentrates. The overall uniform appearance confirms the low seasonal moisture variation finding—a region with high moisture variability would show much greater contrast between wet and dry season composites.
  
  Figure 11: Combined vegetation and SAR analysis visualization providing integrated assessment of surface conditions relevant to road construction. This composite view enables identification of areas where vegetation density and soil moisture conditions may interact to influence construction scheduling.

1. Optimal Construction Window: April through October (dry season) remains preferred for major earthworks, with mean VV backscatter of -11.36 dB indicating consistently dry conditions suitable for all construction activities. This seven-month window provides ample time for major earthworks, base course installation, and surface treatment.
2. Wet Season Work: Unlike many tropical regions, the Pilbara's 0.24 dB seasonal variation suggests that road construction can continue through wet season windows between rainfall events, particularly for surfacing and finishing works. This extended construction season enables more flexible project scheduling and potentially accelerated completion.
3. Cyclone Contingency: While general soil moisture remains stable,
   requiring evacuation protocols and potential short-term work stoppages. Road designs must accommodate these episodic events through adequate drainage infrastructure. Construction contingency planning should include cyclone response procedures aligned with Bureau of Meteorology warning systems.

The Pilbara's ephemeral drainage network—dry creek beds (wadis) that flood violently during cyclonic events—represents the single most significant engineering challenge for access road design. HydroSHEDS flow accumulation analysis identifies concentrated drainage corridors requiring crossing infrastructure:

Figure 12: Flow accumulation visualization derived from HydroSHEDS. Blue indicates high flow accumulation (major drainage channels), light blue shows tributaries, and white represents ridgelines and drainage divides. Road alignments should cross blue features perpendicular where possible, with appropriate culvert or bridge infrastructure. The drainage pattern reveals a dendritic network typical of the dissected plateau landscape, with multiple levels of tributaries feeding into larger channels.

• Engineered low-water crossings or bridges designed for cyclone-magnitude flood events
• Upstream/downstream flood modeling using hydrological design standards
• Emergency closure protocols during extreme events, with alternative route planning
• Scour protection for bridge foundations and culvert outlets Minor Tributary Crossings: Light blue features indicate secondary drainage requiring standard culvert infrastructure. The numerous crossings typical of the dissected plateau landscape necessitate a systematic culvert sizing and placement program. Modern design practice uses probabilistic flood frequency analysis to size culverts for appropriate return period events—typically 1-in-20 year events for minor roads and 1-in-50 year for major access routes. Ridge Alignments: White areas in the flow accumulation map correspond to topographic divides where drainage flows away from the road alignment. Where operationally feasible, routing roads along these ridgelines eliminates drainage crossing requirements entirely—a significant cost and maintenance advantage. Ridge alignments also offer improved visibility, reduced flooding risk, and often coincide with stable terrain conditions.

The comprehensive terrain analysis enables construction of an integrated risk matrix combining slope, drainage, and vegetation factors into a unified assessment framework:

Figure 13: Integrated terrain risk assessment matrix combining slope stability, drainage concentration, and vegetation density factors. This visualization synthesizes multiple data layers to identify optimal routing corridors where all factors align favorably.

Figure 14: Overall road suitability assessment visualization showing the spatial distribution of terrain favorability for 2026 access road development. The extensive green areas confirm that the majority of the study region offers favorable conditions for road construction.

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Score Range	Rating	Pilbara Status
≥80	Excellent/Good	82.8 ✓
60-79	Moderate	—
40-59	Challenging	—
<40	Poor	—
Figure 15: Statistical summary of terrain metrics showing elevation and slope distributions across the study area, providing quantitative context for the suitability assessment.
Figure 16: Cumulative slope distribution curve. The steep initial rise confirms that the majority of terrain falls within low slope categories—reading horizontally at 90% on the y-axis gives the slope value below which 90% of terrain falls. This visualization provides an alternative perspective on the dominance of gentle terrain.
Figure 17: Comprehensive terrain analysis dashboard integrating all key findings into a single visual summary for executive review. This dashboard provides a consolidated view of terrain conditions supporting the "GOOD" suitability rating.

• Great Northern Highway (Newman to Port Hedland): Sealed two-lane route serving as the primary north-south spine for the region
• Marble Bar Road: Access to historically significant mining areas and the town famous for Australia's heat records
• Ripon Hills Road: Eastern Pilbara connectivity serving remote mining operations These routes demonstrate that the Pilbara terrain is demonstrably buildable—existing infrastructure has been successfully constructed and maintained across similar landscapes for decades. However,
  with flash floods causing closures and corrugations developing on unsealed sections.
  , and travel maps warn of extreme caution in flood-prone areas.

• Width:
  to accommodate 200-400 tonne autonomous trucks with adequate safety margins
• Profile: Banked curves, continuously graded surfaces maintained to tight tolerances
• Dust Control: Magnesium chloride binders, frequent water truck applications creating a hardened surface layer
• Adaptations: Terrain-adjusted designs for rocky climbs and soft alluvial flats, with specialized pavement structures for different conditions These haul roads represent the engineering gold standard for Pilbara road construction. While 2026 access roads may not require the same width specifications (depending on vehicle types), the dust suppression, drainage management, and gradient control principles apply directly. The mining industry's decades of experience operating in the Pilbara provides a proven playbook for road construction in this environment.

1. Hope Downs 2 Expansion:
   demonstrate the integration of access road planning with mine development and the scale of ongoing infrastructure investment.
2. BHP/Rio Tinto Shared Infrastructure MOU:
   establishes precedent for optimized road network sharing that may reduce new construction requirements through collaborative infrastructure planning.
3. Electrification Investments:
   handling "Pilbara chaos" (dust, floods, heat) without diesel demonstrates that the region's challenging conditions are manageable with appropriate engineering and that new technologies can be successfully deployed.
4. Infrastructure Investment Priorities: Regional investment debates highlight the balance between
   , indicating the political and economic context within which 2026 road development occurs.

1. Geotechnical Investigation: Bore hole sampling along proposed corridors to validate soil stability assumptions, particularly in marginal stability zones
2. Updated Topographic Survey: LiDAR or drone photogrammetry for final alignment areas, especially near existing mine infrastructure where SRTM data may be outdated
3. Drainage Flow Verification: Field inspection of major drainage crossings to confirm HydroSHEDS model accuracy
4. Cultural Heritage Survey: Indigenous heritage assessment required under Western Australian legislation
5. Weather Station Integration: Real-time rainfall monitoring integration with Bureau of Meteorology data for construction scheduling

1. pilbara_elevation_map.png
2. pilbara_slope_map.png
3. pilbara_stability_classification.png
4. pilbara_aspect_map.png
5. pilbara_hillshade.png
6. pilbara_ndvi.png
7. pilbara_sentinel2_truecolor.png
8. pilbara_sentinel1_sar.png
9. pilbara_drainage_network.png
10. stability_classification_chart.png
11. terrain_statistics_chart.png
12. cumulative_slope_distribution.png
13. terrain_risk_matrix.png
14. road_suitability_assessment.png
15. elevation_profile.png
16. terrain_analysis_dashboard.png
17. vegetation_sar_analysis.png
18. pilbara_aoi.geojson