Dataset Information

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.

SUBMITTER: Leigh C

PROVIDER: S-EPMC6905580 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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

Leigh Catherine C Heron Grace G Wilson Ella E Gregory Taylor T Clifford Samuel S Holloway Jacinta J McBain Miles M Gonzalez Felipé F McGree James J Brown Ross R Mengersen Kerrie K Peterson Erin E EE

PloS one 20191211 12

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 Aust ...[more]

PMID: 31825957

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Project description:BackgroundThe quantification of species-richness and species-turnover is essential to effective monitoring of ecosystems. Wetland ecosystems are particularly in need of such monitoring due to their sensitivity to rainfall, water management and other external factors that affect hydrology, soil, and species patterns. A key challenge for environmental scientists is determining the linkage between natural and human stressors, and the effect of that linkage at the species level in space and time. We propose pixel intensity based Shannon entropy for estimating species-richness, and introduce a method based on statistical wavelet multiresolution texture analysis to quantitatively assess interseasonal and interannual species turnover.Methodology/principal findingsWe model satellite images of regions of interest as textures. We define a texture in an image as a spatial domain where the variations in pixel intensity across the image are both stochastic and multiscale. To compare two textures quantitatively, we first obtain a multiresolution wavelet decomposition of each. Either an appropriate probability density function (pdf) model for the coefficients at each subband is selected, and its parameters estimated, or, a non-parametric approach using histograms is adopted. We choose the former, where the wavelet coefficients of the multiresolution decomposition at each subband are modeled as samples from the generalized Gaussian pdf. We then obtain the joint pdf for the coefficients for all subbands, assuming independence across subbands; an approximation that simplifies the computational burden significantly without sacrificing the ability to statistically distinguish textures. We measure the difference between two textures' representative pdf's via the Kullback-Leibler divergence (KL). Species turnover, or [Formula: see text] diversity, is estimated using both this KL divergence and the difference in Shannon entropy. Additionally, we predict species richness, or [Formula: see text] diversity, based on the Shannon entropy of pixel intensity.To test our approach, we specifically use the green band of Landsat images for a water conservation area in the Florida Everglades. We validate our predictions against data of species occurrences for a twenty-eight years long period for both wet and dry seasons. Our method correctly predicts 73% of species richness. For species turnover, the newly proposed KL divergence prediction performance is near 100% accurate. This represents a significant improvement over the more conventional Shannon entropy difference, which provides 85% accuracy. Furthermore, we find that changes in soil and water patterns, as measured by fluctuations of the Shannon entropy for the red and blue bands respectively, are positively correlated with changes in vegetation. The fluctuations are smaller in the wet season when compared to the dry season.Conclusions/significanceTexture-based statistical multiresolution image analysis is a promising method for quantifying interseasonal differences and, consequently, the degree to which vegetation, soil, and water patterns vary. The proposed automated method for quantifying species richness and turnover can also provide analysis at higher spatial and temporal resolution than is currently obtainable from expensive monitoring campaigns, thus enabling more prompt, more cost effective inference and decision making support regarding anomalous variations in biodiversity. Additionally, a matrix-based visualization of the statistical multiresolution analysis is presented to facilitate both insight and quick recognition of anomalous data.

Project description:BackgroundBehavioral weight loss interventions promote clinically significant weight loss over 12 months, but weight regain remains problematic and a substantial proportion of participants do not achieve long-term weight loss maintenance. Novel methods are needed that instill habit strength for sustaining weight control behaviors long term. Virtual reality (VR) has the potential to provide opportunities within behavioral treatment for patients to practice desired weight control behaviors in the frequency and magnitude necessary to build durable habits. A pilot randomized trial was done to test the feasibility integrating virtual reality (VR) into standard behavioral weight loss treatment.MethodsParticipants were 15 adults (43 years; 46.7% Hispanic), with overweight or obesity who were randomly assigned to a 4-week Standard Behavioral Weight Loss plus Non-Weight-Related VR app (i.e., Control Group) or Standard Behavioral Weight Loss plus Weight-Related VR app (i.e., Intervention Group). The Intervention's VR tool was designed to enable practice of behavioral skills taught in weekly group meetings, including managing social and home environmental cues for eating and activity.ResultsParticipants were recruited over 3 months, and retention at the final assessment visit was high (86.6%). The VR footage and resulting app were rated as highly realistic (6.7 on a 10-point scale), and the VR program overall was rated as highly satisfactory (3.6 on a 4-point scale). Adverse effects of eye strain and motion sickness were minimal (~ 2 on a 7-point scale). As expected, the intervention and control groups both lost weight and unadjusted means (SD) averaged 3.4% (2.7) and 2.3% (3.6), respectively, over the 4 weeks. Overall, participants reported preferring a VR approach above traditional weight loss programs (rating of 5 on a 7-point scale).ConclusionsFuture research is needed to develop and test the feasibility of using VR for other weight control skills with a larger sample size and longer evaluation period to determine if VR can improve standard behavioral weight loss outcomes by intensifying practice opportunities and building habit strength for weight loss maintenance.Trial registrationNCT04534088 ; date of registration: 09/01/2020, retrospectively registered.

Dataset Information

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

Publications

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

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