Sharply increased mass loss from glaciers and ice caps in the Canadian Arctic Archipelago.

Mountain glaciers and ice caps are contributing significantly to present rates of sea level rise and will continue to do so over the next century and beyond. The Canadian Arctic Archipelago, located off the northwestern shore of Greenland, contains one-third of the global volume of land ice outside the ice sheets, but its contribution to sea-level change remains largely unknown. Here we show that the Canadian Arctic Archipelago has recently lost 61 ± 7 gigatonnes per year (Gt yr(-1)) of ice, contributing 0.17 ± 0.02 mm yr(-1) to sea-level rise. Our estimates are of regional mass changes for the ice caps and glaciers of the Canadian Arctic Archipelago referring to the years 2004 to 2009 and are based on three independent approaches: surface mass-budget modelling plus an estimate of ice discharge (SMB+D), repeat satellite laser altimetry (ICESat) and repeat satellite gravimetry (GRACE). All three approaches show consistent and large mass-loss estimates. Between the periods 2004-2006 and 2007-2009, the rate of mass loss sharply increased from 31 ± 8 Gt yr(-1) to 92 ± 12 Gt yr(-1) in direct response to warmer summer temperatures, to which rates of ice loss are highly sensitive (64 ± 14 Gt yr(-1) per 1 K increase). The duration of the study is too short to establish a long-term trend, but for 2007-2009, the increase in the rate of mass loss makes the Canadian Arctic Archipelago the single largest contributor to eustatic sea-level rise outside Greenland and Antarctica.

MBT3D: Deep learning based multi-object tracker for bumblebee 3D flight path estimation.

This work presents the Multi-Bees-Tracker (MBT3D) algorithm, a Python framework implementing a deep association tracker for Tracking-By-Detection, to address the challenging task of tracking flight paths of bumblebees in a social group. While tracking algorithms for bumblebees exist, they often come with intensive restrictions, such as the need for sufficient lighting, high contrast between the animal and background, absence of occlusion, significant user input, etc. Tracking flight paths of bumblebees in a social group is challenging. They suddenly adjust movements and change their appearance during different wing beat states while exhibiting significant similarities in their individual appearance. The MBT3D tracker, developed in this research, is an adaptation of an existing ant tracking algorithm for bumblebee tracking. It incorporates an offline trained appearance descriptor along with a Kalman Filter for appearance and motion matching. Different detector architectures for upstream detections (You Only Look Once (YOLOv5), Faster Region Proposal Convolutional Neural Network (Faster R-CNN), and RetinaNet) are investigated in a comparative study to optimize performance. The detection models were trained on a dataset containing 11359 labeled bumblebee images. YOLOv5 reaches an Average Precision of AP = 53, 8%, Faster R-CNN achieves AP = 45, 3% and RetinaNet AP = 38, 4% on the bumblebee validation dataset, which consists of 1323 labeled bumblebee images. The tracker's appearance model is trained on 144 samples. The tracker (with Faster R-CNN detections) reaches a Multiple Object Tracking Accuracy MOTA = 93, 5% and a Multiple Object Tracking Precision MOTP = 75, 6% on a validation dataset containing 2000 images, competing with state-of-the-art computer vision methods. The framework allows reliable tracking of different bumblebees in the same video stream with rarely occurring identity switches (IDS). MBT3D has much lower IDS than other commonly used algorithms, with one of the lowest false positive rates, competing with state-of-the-art animal tracking algorithms. The developed framework reconstructs the 3-dimensional (3D) flight paths of the bumblebees by triangulation. It also handles and compares two alternative stereo camera pairs if desired.