Using virtual reality and thermal imagery to improve statistical modelling of vulnerable and protected species

Abstract

Biodiversity loss and sparse observational data mean that critical conservation decisions may be based on little to no information. Emerging technologies, such as airborne thermal imaging and virtual reality, may facilitate species monitoring and improve predictions of species distribution. Here we combined these two technologies to predict the distribution of koalas, specialized arboreal foliovores facing population declines in many parts of eastern Australia. For a study area in southeast Australia, we complemented ground-survey records with presence and absence observations from thermal-imagery obtained using Remotely-Piloted Aircraft Systems. These field observations were further complemented with information elicited from koala experts, who were immersed in 360-degree images of the study area. The experts were asked to state the probability of habitat suitability and koala presence at the sites they viewed and to assign each probability a confidence rating. We fit logistic regression models to the ground survey data and the ground plus thermal-imagery survey data and a Beta regression model to the expert elicitation data. We then combined parameter estimates from the expert-elicitation model with those from each of the survey models to predict koala presence and absence in the study area. The model that combined the ground, thermal-imagery and expert-elicitation data substantially reduced the uncertainty around parameter estimates and increased the accuracy of classifications (koala presence vs absence), relative to the model based on ground-survey data alone. Our findings suggest that data elicited from experts using virtual reality technology can be combined with data from other emerging technologies, such as airborne thermal-imagery, using traditional statistical models, to increase the information available for species distribution modelling and the conservation of vulnerable and protected species.

Comparing UAS LiDAR and Structure-from-Motion Photogrammetry for Peatland Mapping and Virtual Reality (VR) Visualization

“…Because peatlands are both fragile ecosystems and in general have poor accessibility, tools to remotely study, access, and visualize peatland structure in 3D are needed for advancing our understanding of their response to climate change. Although not a new technology [28], the recent advances in virtual reality (VR) [29], with its applications in medicine [30], conservation [31], geosciences [32,33], e-tourism [34,35], and education [36], among others, provide novel opportunities to study peatlands and other ecosystems remotely without disturbance [37]. VR is technology (hardware and software) that generates a simulated environment which stimulates a "sense of being present" in the virtual representation [38].…”

Real-time drone derived thermal imagery outperforms traditional survey methods for an arboreal forest mammal

“…These rely on evidence of occupancy and activity such as scratch marks, scats, citizen sightings, and bellows [3,20,22,24,25]. In contrast, the application of direct techniques, such as diurnal strip-width [10,26,27] and line-transects [10], nocturnal line-transects [3], and emerging remotely piloted aircraft system thermal imaging (RPAS, also known as drones) technologies [8,[28][29][30], require surveyors to physically sight koalas and this is preferable for density and population estimates [3].…”

“…For example, RPAS can be coupled with sensors and infrared detectors that capture high-resolution thermal images [41]. RPAS are particularly promising for surveying wildlife [42,43] and have been accurate and cost-effective in determining abundance [43] and for detecting a range of species including arboreal mammals such as monkeys [44] and koalas [8,[28][29][30].…”

“…Coincidently, our RPAS method has some parallels with the method described in Leigh et al 2019 [30] which surveyed 20 ha of parkland vegetation (dense forest, open scrubland, grass fields) in south-east Queensland. In this study, which we were not aware of at the time of publishing Beranek et al 2020 [29] or for the design of the present study, RPAS koala detections were ground truthed using in field observations from diurnal line-transects and were then combined with expert elicitation and public observation data to develop a statistical model that better predicts koala distribution [30].…”

“…RPAS thermal imaging technology has achieved koala detection in all seasons of the year in parkland and plantations, but has only achieved detection of wild koalas in natural bushland in winter [29], and this work was completed alongside the present study. RPAS thermal imaging technology for both the manual and automated detection of koalas is still in development, seasonal differences in precision and koala detection at variable speed and altitude have not been investigated [8,[28][29][30], and machine learning algorithms require further training [8,28]. Despite this, and a claim of 'higher precision achieved' against traditional ground survey methods [8,28], RPAS-derived methods have not been formally compared to other more widely used techniques.…”