Using Ozius’ Naxia analytics engine to improve our knowledge of Littoral (blue carbon) Ecosystems: mangroves and seagrass meadows.
NASA USGS Landsat 7/ 8
ESA Sentinel 2A/B
DigitalGlobe WorldView 2
Deakin University marine ecologist Dr Peter Macreadie, as quoted by ABC News Australia
Blue Carbon storage from Seagrass can persist for millennia and is highly efficient.
(Fourqurean et al. 2012; Macreadie et al. 2014; Howard et al. 2016, Mateo et al. 2006; Fourqurean et al. 2012; Howard et al. 2016).
Mangroves have an annual economic value of approximately US$ 200,000-900,000 per square kilometre
(UNEP- WCMC 2006), and their extent is closely correlated to the success of adjacent fisheries (Manson et al. 2005; Aburto- Oropeza et al. 2008).
Image: Global distribution of coral, mangrove, and seagrass diversity. (Image created by Philippe Rekacewicz in May 2002 from data compiled by UNEP-WCMC, 2001. Reproduced from Jennerjahn 2012 with permission from Elsevier)
Landform characterisation Habitat
Community and Economic uses
1. Mangrove speciation, extent, density
2. Benthic habitat monitoring
3. Ocean uses – aquaculture hotspots
Turbid Lake, New South Wales, Australia
Ozius undertook an assessment of seagrass extent and cover within a turbid waterbody in New South Wales. The project utilised high resolution imagery and field transect validation data to successfully map the extent and cover classes of seagrass.
Methodologies followed the use of remote sensing classification algorithms to produce efficient and reliable data sets for the further use in management decisions, and to assess the overall extent of seagrass within the lake.
Seagrass communities are composed of predominantly Zostera spp., Halophila spp., and Ruppia spp.. The catchment of Lake Illawarra is a region of highly intensive land uses and its shores are heavily urbanised. The seagrass density classes were modelled using a continuous data method to maintain the relationship between spectral signature and seagrass density class. The output was then classified into four density class themes, followed by clustering into a binary density theme as described in Table 2.
Integrating Historic Field Data
By integrating historic field data, operational activities and natural events to create a complete and accurate trend analysis to guide future decisions and historic reporting requirements.