Augmenting rangeland condition monitoring with drones: potential for carbon farming projects to support widespread ecosystem assessment

Le résumé fourni par la source

Large areas of the Australian rangelands are used for pastoral agriculture. Rangeland condition monitoring commonly operates at one of the following two scales: (1) detailed but spatially restricted on-ground surveys of farm and ecosystem condition, or (2) regional and national analyses using data from earth observation satellites such as Landsat or Sentinel-2. This study aimed to assess the potential of drone-based data collection from carbon farming projects to augment rangeland condition monitoring. The widespread adoption of carbon farming in Australian rangelands and the increasing use of drones to validate vegetation maps could provide an opportunity to bridge these two scales. Range condition was defined in terms of plant species composition, woody vegetation structure, and ground cover, focusing on indicators relevant to both carbon sequestration and broader ecosystem function. Existing maps of vegetation structure were combined with on-ground surveys and drone data collected at the scale of an individual rangeland property. Drone-mounted sensors produced ultra-high-resolution imagery (~2 cm GSD) and three-dimensional point clouds. This combination of imagery and three-dimensional data enabled measurement of condition indicators such as tree size distributions, canopy area and ground layer coverage. This case study of drone-based ecological profiling after land management change demonstrated that monitoring programmes of carbon farming projects can be leveraged for detailed and accurate investigation of ecosystem condition. Significant differences were found in canopy and ground layer elements between vegetation communities and land-use histories. These findings have highlighted the value of repurposing carbon farming project data for holistic landscape condition assessment. The value and importance of in situ vegetation structural measurements is further emphasised by the contrasting unsuitability of national-scale satellite remote-sensing products for precise property-level mapping. The potential for the growing in situ dataset to support both property-scale decision-making and understanding of the broader ecological effects of woody vegetation regrowth is discussed.

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