Endangered species monitoring and conservation technology
September 13, 2022
5 min read
The Australian Malleefowl (Leipoa ocellata) are large ground-dwelling birds that form large nesting mounds of leaf litter to incubate their eggs.
The Malleefowl belongs to the Megapodiidae family, and unlike other megapodes (mound builders), they are found in semi-arid landscapes. Malleefowl are listed as vulnerable under the Environment Protection and Biodiversity Conservation Act 1999 (EPBC Act) because their distribution has declined significantly due to the disruption of habitat through grazing pressure from sheep and cattle as well as competition for food resources with introduced species such as foxes and cats that prey on eggs.
The National Recovery plan introduced in 2007 sets out the actions necessary to stop the decline and support the recovery of the listed threatened species.
The occurrence of Malleefowl mounds is typically used as a proxy for the bird's occurrence. The conventional method for monitoring Malleefowl populations has several limitations, the most significant of which is the difficulty of detecting mounds across a large area. This has traditionally meant costly and time-consuming ground surveys. However, Malleefowl frequently reuse mound nests over several years, so ecologists needed a more efficient way to detect new and existing mound sites.
Today digital technologies such as remote sensing mound detection and nest surveying are being used to enhance the monitoring of endangered Malleefowl species, helping better track populations over large areas. The new Malleefowl mound detection approach uses a type of aerial mapping technology called LiDAR (light detection and ranging) to model the topography of the ground's surface at a fine scale. LiDAR data is captured using lidar sensors fitted to a light aircraft.
On receipt of the LIDAR data and high-resolution imagery, Anditi will check for gaps and any other issues in the data. In the event that no issues are found, Anditi will utilise the Anditi data processing engine to classify the data into ground and non-ground classifications to create an accurate DEM ( digital elevation model). The data will then be further analysed to identify Malleefowl mounds. These are found using Anditi’s patented near-ground feature detection algorithms to identify potential sites, which are then ranked depending on the degree of certainty. Certainty is affected by the mound's age, intactness, overly dense obscuring vegetation and other factors.
The Anditi Malleefowl mound analysis algorithms look for ground features in the point cloud that best approximate a typical Malleefowl mound shape. Based on the algorithm match to shape and manual checks, a mound is classed from 1 to 4.
Our detection process combines rapid analysis of large data sets. Where available, aerial photography is incorporated into the software to provide additional information on habitat and landscape context for the area surrounding candidate mounds. This reduces the costs and time required for field surveys. LiDAR technology isn’t new – it has long been used for everything from mapping the ocean floor to assisting in emergency response situations – but its application for ecological monitoring has become an emerging field over the past decade. Thanks to the emerging adoption of LIDAR over the past decade, Anditi has delivered over 64 Malleefowl projects and identified over 1,000 mounds under categories 1 & 2 ( highly likely mounds ).
In March 2021 NSW Saving Our Species (SOS) strategy; commissioned Anditi to analyse and classify 20000km² of the Mid and West NSW to find potential mound sites. SOS Project Sites
Project Results- LiDAR has enabled the program to identify 518 prominent and 67 potential mounds by size and shape using Anditi's patented algorithm.
Great Victorian Desert
In December 2018, the Great Victoria Desert Biodiversity Trust Fund (GVDBT) and AngloGold Ashanti Australia Limited contracted Anditi for aerial laser scanning and aerial photography of 1675 km of 600 m wide corridors, totalling approximately 1005 km² of Western Australia as a precursor to finding existing and new Malleefowl mounds through automated analysis. The aerial survey was undertaken in extremely hot conditions in January 2019. The resultant point cloud totalled 40GB of data. Once the raw laser point cloud was processed to a 3D LAS point cloud, Anditi could load the point cloud into our Anditi Engine software and classify the data for analysis for Malleefowl mound-like features using our proprietary smart algorithms. A comprehensive report was compiled for the clients covering all facets of the survey on project completion.
Project Results - Over 100 potential Malleefowl mounds were found. Having a location for potential mounds means that field researchers can go directly to these locations in a huge area of the arid, remote country to verify and determine whether the mounds are active. This saves the huge effort of trying to find the proverbial “needle in the haystack” via large ground crews. It increases effectiveness, value for scarce environmental funding and safety.