Multi-decadal coverage of giant kelp (Macrocystis pyrifera) in Tasmania derived from satellite imagery

Giant kelp (Macrocystis pyrifera) forests are a foundational habitat that dominates many nearshore rocky coastlines in temperate and cold-water regions worldwide. Macrocystis forests can extend as much as 40 m to the surface and form closed canopies that alter the light, current, and sedimentation environment beneath them. They play an ecosystem-structuring function and provide habitat for a diverse range of fish and invertebrate species. The coastal waters of Tasmania represent the most extensive giant kelp habitat in Australia. Dense forests have historically covered large areas of the nearshore reef habitat, but significant declines have been recorded in eastern Tasmania in recent decades. These losses are thought to be linked to environmental stressors such as extended periods of high water movement, warming sea temperatures, and nutrient depletion. Eastern Tasmania has experienced some of the most pronounced declines, leading to the 2012 listing of the giant kelp community as an ‘endangered marine community type’ under Australia’s Federal Environment Protection and Biodiversity Conservation Act. This dataset uses Landsat satellite imagery to quantifiy the spatial extent and temporal variability of giant kelp surface canopies along the Tasmanian coastline to address gaps in long-term monitoring. The data collection is divided into two components: 1) A statewide analysis that maps the extent of Macrocystis pyrifera canopies across the whole Tasmanian coastline, grouped into nine three-year bins spanning 1987–2015. This long-term dataset is intended for assessing long-term (decadal) changes at a broad spatial scale. 2) A higher temporal-resolution analysis of 24 specific sites along the Tasmanian coastline. This dataset includes canopy coverage from all cloud-free Landsat imagery captured over the period 1986-2015 to enable detailed analysis of seasonal and interannual fluctuations in kelp canopy extent and understand localised population dynamics.