Crevasse extraction at the end of Baishui River Glacier No.1 in Yulong Snow Mountain based on orthophoto unmanned aerial vehicle(UAV)image
Glaciers,as an integral part of the cryosphere,are highly susceptible to both local and global climate change.Ice crevasses,which are prominent features on the surface of glaciers and the important channels for gla-cier meltwater,play a crucial role in understanding the condition,stability,internal stress and mass balance of glaciers.Mountain glaciers are subject to cloud cover and area limitation,and the spatial resolution of traditional satellite remote sensing data is low,which is difficult to be used for extracting ice crevasses,so there are fewer studies related to ice crevasses on mountain glaciers.In this study,the objective was to address the challenge of identifying and extracting glacier crevasses quickly and accurately.This research takes the mountain glacier:Baishui River Glacier No.1 in Yulong Snow Mountain in Lijiang,Yunnan Province as the research object,and takes the cloud-free orthophotos of the glacier surface with a resolution of 0.12 m in 2021 and 2022 acquired by aerial photography of the DJI M300RTK drone as the data source,and applies the U-Net Deep Learning Net-work to carry out the extraction of ice crevasses of the Baishui River Glacier No.1.The results demonstrate that the U-Net network outperforms traditional methods such as the Canny operator and SVM algorithm in terms of crevasse extraction accuracy.The overall accuracy can be as high as 93%.Fur-thermore,the U-Net network exhibits strong generalization capabilities,which can be used to automatically ex-tract unmanned aerial imagery from different time periods.From the perspective of spatial distribution of ice cre-vasses,the crevasses observed on BRG1 predominantly consist of transverse crevasses,splaying crevasses,and En échelon crevasses,which show the typical characteristics of mountain glacier ice crevasses in the low advec-tion lifecycle.As the altitude decreases,there is a gradual transition from transverse crevasses to splaying cre-vasses.From the perspective of temporal change of ice crevasses,comparing the extraction results from differ-ent time periods reveals an increase in the number and average length of crevasses.This proves that the ablation of BRG1 is intensifying,and the glacier mass is gradually losing.The orientation of the ice crevasses was al-most unchanged,indicating that the stress inside the glacier didn't change dramatically.In summary,the study of intelligent extraction of ice crevasses based on UAV images and deep learning methods creates new possibili-ties for extracting ice crevasses from mountain glaciers,and can provide technical support for monitoring glacier changes and their relationship with climate change.
crevasseunmanned aerial vehicles(UAV)U-NetBaishui River Glacier No.1
NETL
NSTL
万方数据
