Estimation of vegetative fuel loads using Landsat TM imagery in New South Wales, Australia

Fuel loads in forest areas are dependent on vegetation type and the time since the last fire. This paper reports a study on the feasibility of using remotely sensed data to estimate vegetative fuel loads. It describes two methods for estimating fuel loads using Landsat TM data based on equations describing litter accumulation and decomposition. The first method uses classification techniques to predict vegetation types coupled with fire history data to derive current fuel loads. The second method applies a canopy turnover rate to estimate litterfall and subsequently accumulated litter from biomass, thus utilising the dominant influence of canopy on remotely sensed data. Both methods are compared with data collected from Popran National Park in coastal New South Wales. The amounts of litter calculated with the biomass method were similar to field results, but the classification method was found to overestimate fuel loads. A sensitivity analysis investigated the impact of varying the vegetation constants and rates used in the fuel estimates to simulate uncertainty or error in their values. The biomass method was less subject to uncertainties and has potential for estimating fuel quantities to provide useful spatial information for fire managers.10 page(s

Offline Imagery Checks for Remote Drone Usage

Drones are increasingly used for a wide range of applications including mapping, monitoring, detection, tracking and videography. Drone software and flight mission programs are, however, still largely marketed for “urban” use such as property photography, roof inspections or 3D mapping. As a result, much of the flight mission software is reliant upon an internet connection and has built-in cloud-based services to allow for the mosaicking of imagery as a direct part of the image collection process. Another growing use for drones is in conservation, where drones are monitoring species and habitat change. Naturally, much of this work is undertaken in areas without internet connection. Working remotely increases field costs, and time in the field is often aligned with specific ecological seasons. As a result, pilots in these scenarios often have only one chance to collect appropriate data and an opportunity missed can mean failure to meet research aims and contract deliverables. We provide a simple but highly practical piece of code allowing drone pilots to quickly plot the geographical position of captured photographs and assess the likelihood of the successful production of an orthomosaic. Most importantly, this process can be performed in the field with no reliance on an internet connection, and as a result can highlight any missing sections of imagery that may need recollecting, before the opportunity is missed. Code is written in R, a familiar software to many ecologists, and provided on a GitHub repository for download. We recommend this data quality check be integrated into a pilot’s standard image capture process for the dependable production of mosaics and general quality assurance of drone collected imagery

Colonial waterbird breeding in Australia: wetlands, water requirements and environmental flows

Colonial waterbirds are particularly dependent on river flows for the critical breeding stage of their lifecycle. They breed in response to large flows on relatively few wetlands in Australia. Most species of colonial waterbirds require sufficient river flows, flooding and availability of suitable nesting habitat. Water resource development through water extraction and impoundment is degrading wetlands around the world, changing the natural flow regime and affecting aquatic organisms, including colonially breeding waterbirds that rely on wetland inundation. To overcome some of the significant impacts of water resource development, there is increasing focus on the management of environmental flows for ecosystems and specific organisms. Colonial waterbirds are increasingly important as a target group of organisms for the management of environmental flows, providing a measure of the success or failure of environmental flow management. My thesis examined the breeding of colonial waterbirds in Australia at a range of scales and the importance of environmental flow management. Chapter 1 set the context by briefly reviewing the impacts of water resource development and its threat to colonial waterbird breeding and then summarising each of the subsequent chapters. Chapter 2 examined the historical use (1899-2008) of wetlands for breeding by colonial waterbirds in Australia and characterised the types of wetlands used for breeding. It also identified important sites in Australia for breeding by colonial waterbirds their characteristics and assessed vulnerability to water resource development. In Chapter 3, I focussed on Narran Lakes, one of Australia’s most important colonial waterbird breeding sites and assessed the impact of water resource development on ibis breeding over the period 1921-2008. In Chapter 4, I examined the success of the 2008 ibis breeding event at Narran Lakes when, because of declining water levels, a significant volume of water was purchased to ensure that the breeding colony was successful. Finally, in Chapter 5, I reviewed the stimulus and breeding responses of colonially breeding waterbirds in Australia with the aim of identifying the key elements of environmental flows required to successfully manage colonial waterbird breeding

Counting Mixed Breeding Aggregations of Animal Species Using Drones: Lessons from Waterbirds on Semi-Automation

Using drones to count wildlife saves time and resources and allows access to difficult or dangerous areas. We collected drone imagery of breeding waterbirds at colonies in the Okavango Delta (Botswana) and Lowbidgee floodplain (Australia). We developed a semi-automated counting method, using machine learning, and compared effectiveness of freeware and payware in identifying and counting waterbird species (targets) in the Okavango Delta. We tested transferability to the Australian breeding colony. Our detection accuracy (targets), between the training and test data, was 91% for the Okavango Delta colony and 98% for the Lowbidgee floodplain colony. These estimates were within 1–5%, whether using freeware or payware for the different colonies. Our semi-automated method was 26% quicker, including development, and 500% quicker without development, than manual counting. Drone data of waterbird colonies can be collected quickly, allowing later counting with minimal disturbance. Our semi-automated methods efficiently provided accurate estimates of nesting species of waterbirds, even with complex backgrounds. This could be used to track breeding waterbird populations around the world, indicators of river and wetland health, with general applicability for monitoring other taxa

Data from: Monitoring large and complex wildlife aggregations with drones

Recent advances in drone technology have rapidly led to their use for monitoring and managing wildlife populations but a broad and generalised framework for their application to complex wildlife aggregations is still lacking. We present a generalised semi-automated approach where machine learning can map targets of interest in drone imagery, supported by predictive modelling for estimating wildlife aggregation populations. We demonstrated this application on four large spatially complex breeding waterbird colonies on floodplains, ranging from ~20,000 to ~250,000 birds, providing estimates of bird nests. Our mapping and modelling approach was applicable to all four colonies, without any modification, effectively dealing with variation in nest size, shape, colour and density and considerable background variation (vegetation, water, sand, soil etc.). Our semi-automated approach was between 3 to 8 times faster than manually counting nests from imagery at the same level of accuracy. This approach is a significant improvement for monitoring large and complex aggregations of wildlife, offering an innovative solution where ground counts are costly, difficult or not possible. Our framework requires minimal technical ability, is open-source (Google Earth Engine and R), and easy to apply to other survey

Monitoring large and complex wildlife aggregations with drones

Recent advances in drone technology have rapidly led to their use for monitoring and managing wildlife populations but a broad and generalised framework for their application to complex wildlife aggregations is still lacking. We present a generalised semi-automated approach where machine learning can map targets of interest in drone imagery, supported by predictive modelling for estimating wildlife aggregation populations. We demonstrated this application on four large spatially complex breeding waterbird colonies on floodplains, ranging from c. 20,000 to c. 250,000 birds, providing estimates of bird nests. Our mapping and modelling approach was applicable to all four colonies, without any modification, effectively dealing with variation in nest size, shape, colour and density and considerable background variation (vegetation, water, sand, soil, etc.). Our semi-automated approach was between three and eight times faster than manually counting nests from imagery at the same level of accuracy. This approach is a significant improvement for monitoring large and complex aggregations of wildlife, offering an innovative solution where ground counts are costly, difficult or not possible. Our framework requires minimal technical ability, is open-source (Google Earth Engine and R), and easy to apply to other surveys

Eulimbah ibis colony drone imagery

Orthorectified image mosaic derived from drone (Phantom 3 professional, stock camera) imagery, processed via Pix4D Mapper. Location is Eulimbah on the Murrumbidgee River in New South Wales, Australia. The imagery was acquired in October 201