Recognise and interpret spatial patterns of land cover change at different scales using remotely sensed data

What this dot point is asking

QCAA wants you to be able to read remotely sensed data (satellite and aerial imagery) and use it to recognise where, when and how fast land cover has changed. This is a skills dot point as much as a content one: you describe spatial patterns (where change is concentrated, its shape, its direction over time) and you understand how the imagery is produced and analysed. The command word here is closer to "recognise and interpret", so you must read patterns from imagery, not just describe the technology. Strong answers use the language of pattern (clustered, linear, dispersed), name the data source, and connect the pattern to the process driving it.

The answer

What remote sensing is

Remote sensing gathers information about the Earth's surface without direct contact, using sensors on satellites, aircraft and drones. Sensors record how much energy each part of the surface reflects or emits across different wavelengths. Because vegetation, water, soil and built surfaces reflect differently, especially in infrared, software can classify the imagery into land cover types. Repeating the process over years produces a time series that reveals change.

Key data sources

• Landsat. A continuous record of moderate-resolution imagery running since the 1970s, ideal for tracking decades of land cover change.
• Sentinel. The European program providing frequent, higher-resolution imagery for recent monitoring.
• Aerial photography and drones. Higher detail over small areas, useful for local fieldwork.
• Derived products. National datasets in Australia turn raw satellite data into woody vegetation and land cover layers used to monitor clearing.

Change detection and vegetation indices

Change detection compares imagery from two or more dates to map what has changed. Vegetation indices such as the normalised difference vegetation index use the contrast between red and near-infrared reflectance to measure how green and dense vegetation is. A drop in the index between dates flags vegetation loss; a rise flags regrowth. These tools let geographers map clearing, fire scars, drought stress and urban expansion objectively and repeatedly.

Reading spatial patterns

The point of the imagery is the pattern. Describe it with geographic language:

• Distribution: is change clustered in one area, dispersed across many sites, or concentrated along a frontier?
• Shape: is it linear (following roads, rivers or coastlines), patchy and fragmented, or a solid block?
• Direction and rate over time: is the cleared area expanding outward, and how fast?
• Association: does the pattern follow a driver, such as clearing fanning out from new roads, or urban growth spreading along transport corridors?

The Amazon arc of deforestation is a classic pattern: clearing concentrated along a curved frontier following roads into the forest. South East Queensland shows urban expansion as a spreading, coalescing pattern along the coast and transport corridors. The brigalow belt shows large blocks of cleared cropland with fragmented remnant patches.

Scale

Land cover patterns look different at different scales. Local imagery shows the shape of one clearing or suburb. Regional imagery shows how many clearings accumulate into a transformed bioregion. Continental and global imagery shows the overall distribution of forest loss, urban growth and drought. Strong answers state the scale and note that local conversions sum into the regional and global pattern.

Examples in context

Example 1. Queensland clearing monitoring. Decades of Landsat imagery reveal woody vegetation loss as large cleared blocks across the brigalow belt, with rates rising and falling in step with changes in vegetation management policy.

Example 2. Amazon arc of deforestation. Satellite change detection shows clearing concentrated along a curved frontier that advances as new roads open access, a clearly linear and expanding spatial pattern.

Example 3. South East Queensland urban growth. Time-series imagery shows built-up area spreading and coalescing along the coast and major transport corridors, converting farmland and bushland into a continuous urban region.