Abstract
Kernel-based home range models are widely-used to estimate animal habitats and develop conservation strategies. They provide a probabilistic measure of animal space use instead of assuming the uniform utilization within an outside boundary. However, this type of models estimates the home ranges from animal relocations, and the inadequate locational data often prevents scientists from applying them in long-term and large-scale research. In this paper, we propose an end-to-end deep learning framework to simulate kernel home range models. We use the conditional adversarial network as a supervised model to learn the home range mapping from time-series remote sensing imagery. Our approach enables scientists to eliminate the persistent dependence on locational data in home range analysis. In experiments, we illustrate our approach by mapping the home ranges of Bar-headed Geese in Qinghai Lake area. The proposed framework outperforms all baselines in both qualitative and quantitative evaluations, achieving visually recognizable results and high mapping accuracy. The experiment also shows that learning the mapping between images is a more effective way to map such complex targets than traditional pixel-based schemes.
Highlights
The basic concept of the home range is defined as the area traversed by the animal during its normal activities of food gathering, mating, and caring for young [1]
Our method enables scientists to carry out their home range analysis even if the GPS data is insufficient for long-term and large-scale research
Concerning the two deep learning models, we find that both Convolutional Neural Network (CNN) + 2 and Conditional Variational Autoencoder (CVAE) eliminate random noise but suffer the problem of blurring
Summary
The basic concept of the home range is defined as the area traversed by the animal during its normal activities of food gathering, mating, and caring for young [1]. Estimating home range is an important part of investigating species status, analyzing habitat selection, and developing conservation strategies [2]. A series of kernel-based probabilistic methods have shown the advantages in habitat studies [5,6], especially in understanding the internal structure of spatially heterogeneous environments. This type of model [2,7,8] produces a two-dimensional probability density map to represent the probability of animals occurring at each location in the defined area. [9] demonstrates that kernel home range models would enhance the studies of animal movements, species interactions, and resource selection Ref. [9] demonstrates that kernel home range models would enhance the studies of animal movements, species interactions, and resource selection
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