Spatiotemporal variation of anthropogenic drivers predicts the distribution dynamics of Hainan gibbon

Zhang, Yuke; Yu, Jia; Lin, Siliang; He, Jiekun; Xu, Yang; Tu, Jiahao; Jiang, Haisheng

doi:10.1016/j.gecco.2023.e02472

Cited by 1 publication

(1 citation statement)

References 48 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The Hainan gibbon (Nomascus hainanus) is the rarest primate species in the world [2]. Habitat transformation, natural forest cover, landscape shape index, and distance to the nearest roads-Zhang et al [3] pointed out that these four factors led to a dramatic decline of Hainan gibbons. There are only 37 remaining Hainan gibbons in the world, living in approximately 15 square kilometers of forest fragments in the Bawangling National Nature Reserve in Hainan province, China [4].…”

Section: Introductionmentioning

confidence: 99%

Observing Individuals and Behavior of Hainan Gibbons (Nomascus hainanus) Using Drone Infrared and Visible Image Fusion Technology

Li,

Wang,

Zhang

et al. 2023

Drones

View full text Add to dashboard Cite

The Hainan gibbon (Nomascus hainanus) is one of the most endangered primates in the world. Infrared and visible images taken by drones are an important and effective way to observe Hainan gibbons. However, a single infrared or visible image cannot simultaneously observe the movement tracks of Hainan gibbons and the appearance of the rainforest. The fusion of infrared and visible images of the same scene aims to generate a composite image which can provide a more comprehensive description of the scene. We propose a fusion method of infrared and visible images of the Hainan gibbon for the first time, termed Swin-UetFuse. The Swin-UetFuse has a powerful global and long-range semantic information extraction capability, which is very suitable for application in complex tropical rainforest environments. Firstly, the hierarchical Swin Transformer is applied as the encoder to extract the features of different scales of infrared and visible images. Secondly, the features of different scales are fused through the l1-norm strategy. Finally, the Swing Transformer blocks and patch-expanding layers are utilized as the decoder to up-sample the fusion features to obtain the fused image. We used 21 pairs of Hainan gibbon datasets to perform experiments, and the experimental results demonstrate that the proposed method achieves excellent fusion performance. The infrared and visible image fusion technology of drones provides an important reference for the observation and protection of the Hainan gibbons.

show abstract

Section: Introductionmentioning

confidence: 99%