Spatiotemporal changes in early human land use during the Holocene throughout the Yangtze River Basin, China

The contribution of early human activity to the increase in atmospheric CH4 content during the middle to late-Holocene is still debated. The quantitative reconstruction of past changes in land use by early rice agriculture is a key to resolving the issue, because large uncertainties still exist in current prehistoric land use estimates, owing to a lack of direct records. In this study, we used the combination of archaeological data (the area and distribution of archaeological sites) and an improved prehistoric land use model (PLUM) to determine the spatiotemporal changes in land use by rice agriculture throughout the Yangtze River Valley, China, which was the origin and centre of the development of rice cultivation. The results indicate that the area devoted to rice agriculture increased during 10–2 ka BP, and that a significant increase occurred at ~5 ka BP accompanied by a spatial expansion from the northern part of the valley to the entire valley. However, the rice land use area decreased slightly during 4–3 ka BP but then increased after 3 ka BP. We estimate that the CH4 emissions from the rice cultivated area in the Yangtze River Valley increased from ~0.001 (±0.001) to ~1.3 (±0.6) Tg/year during 10–2 ka BP, and the resulting atmospheric CH4 concentrations increased from ~0.004 (±0.002) to ~4.1 (±2.0) ppb, which accounted for 3 (±2)–9 (±5) % of the ‘anomalous atmospheric CH4 increase’ during 5–2 ka BP.

Comparative Study of Markerless Vision-Based Gait Analyses for Person Re-Identification

The model-based gait analysis of kinematic characteristics of the human body has been used to identify individuals. To extract gait features, spatiotemporal changes of anatomical landmarks of the human body in 3D were preferable. Without special lab settings, 2D images were easily acquired by monocular video cameras in real-world settings. The 2D and 3D locations of key joint positions were estimated by the 2D and 3D pose estimators. Then, the 3D joint positions can be estimated from the 2D image sequences in human gait. Yet, it has been challenging to have the exact gait features of a person due to viewpoint variance and occlusion of body parts in the 2D images. In the study, we conducted a comparative study of two different approaches: feature-based and spatiotemporal-based viewpoint invariant person re-identification using gait patterns. The first method is to use gait features extracted from time-series 3D joint positions to identify an individual. The second method uses a neural network, a Siamese Long Short Term Memory (LSTM) network with the 3D spatiotemporal changes of key joint positions in a gait cycle to classify an individual without extracting gait features. To validate and compare these two methods, we conducted experiments with two open datasets of the MARS and CASIA-A datasets. The results show that the Siamese LSTM outperforms the gait feature-based approaches on the MARS dataset by 20% and 55% on the CASIA-A dataset. The results show that feature-based gait analysis using 2D and 3D pose estimators is premature. As a future study, we suggest developing large-scale human gait datasets and designing accurate 2D and 3D joint position estimators specifically for gait patterns. We expect that the current comparative study and the future work could contribute to rehabilitation study, forensic gait analysis and early detection of neurological disorders.