Development of the Yangtze delta during the late Holocene, and its relationship to human activities in the drainage basin, was analyzed using data from 16 cores collected from distributaries to the prodelta. We used AMS 14C dating and digital elevation model (DEM) data from marine charts from 1864 through 2005 to determine ages and estimate sediment accumulation rates. The results demonstrate that the latest major subaqueous delta front formed within the past c. 0.8 cal. ka and features remarkably high accumulation rates (1—4 cm/yr) in comparison with those of previous delta fronts. We also examined the temporal distribution of grain size and magnetic susceptibility in all 16 cores. Results show soil-derived superparamagnetic (SP) minerals generally occur, and even dominate, in the recent ( c. 1.7 cal. ka) Yangtze delta fine-grained sediment, as shown by high values of frequency-dependent magnetic susceptibility (both χFD and χFD%). Rock-derived magnetite dominates generally in the river channel and delta front sand bodies as a result of hydrodynamic sorting, but is also enriched in both fine and coarse-grained sediment formed more recently ( c. 0.8 cal. ka), as evidenced by rising values of mass specific magnetic susceptibility (χLF). SP grains were deposited as early as the late Neolithic, possibly indicating local deforestation associated with the use of fire at that time. We suggest major deforestation in the drainage basin started c. 1.7 cal. ka BP, and intensified after c. 0.8 cal. ka BP when both χLF and χFD show the highest values. We therefore conclude that upland deforestation and cultivation as a result of the migration of human populations from northern China since c. 1.7 cal. ka BP resulted in increased sediment discharge of the Yangtze and played an important role in recent delta construction.
Achieving high-quality surface profiles under strong ambient light is challenging in fringe projection profilometry (FPP) since ambient light inhibits functional illumination from exhibiting sinusoidal stripes with high quantization levels. Conventionally, large-step phase shifting approaches are presented to enhance the anti-interference capability of FPP, but the image acquisition process in these approaches is highly time-consuming. Inspired by the promising performance of deep learning in optical metrology, we propose a deep learning-enabled anti-ambient light (DLAL) approach that can help FPP extract phase distributions from a single fringe image exposed to unbalanced lighting. In this work, the interference imposed by ambient light on FPP is creatively modeled as ambient light-induced phase error (ALPE). Guided by the ALPE model, we generate the dataset by precisely adjusting the stripe contrast before performing active projection, overcoming the challenge of collecting a large sample of fringe images with various illumination conditions. Driven by the novel dataset, the generated deep learning model can effectively suppress outliers among surface profiles in the presence of strong ambient light, thereby implementing high-quality 3D surface imaging. Experimentally, we verify the effectiveness and adaptability of the proposed DLAL approach in both indoor and outdoor scenarios with strong irradiation.
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