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A joint Chinese-Swedish field campaign of Scheimpflug continuous-wave lidar monitoring of rice-field flying pest insects was pursued in very hot July weather conditions close to Guangzhou, China. The occurrence of insects, birds and bats with almost 200 hours of round-the-clock polarization-sensitive recordings was studied. Wing-beat frequency recordings and depolarization properties were used for target classification. Influence of weather conditions on the flying fauna was also investigated.

A versatile mobile remote sensing system for multidisciplinary environmental monitoring tasks on the Chinese scene is described. The system includes a 20 Hz Nd:YAG laser-pumped dye laser, optical transmitting/receiving systems with a 30 cm and a 40 cm Newtonian telescope, and electronics, all integrated in a laboratory, installed on a Jiefang truck. Results from field experiments on atomic mercury DIAL mapping and remote laser-induced fluorescence and break-down spectroscopy are given.

The Qushandao Granite, mainly composed of alkali‐feldspar granite, is situated in the eastern Zhejiang province of coastal southeast China. In this paper, we present whole‐rock geochemistry, zircon U–Pb geochronology, and Hf isotopes to constrain the age, magma sources, and geodynamic setting of the Qushandao Granite. LA‐ICP‐MS zircon U–Pb dating results revealed that the Qushandao Granite was emplaced in the Late Cretaceous (101–98 Ma). Geochemically, the Qushandao Granite exhibits relatively high silica and alkali contents, metaluminous to weakly peraluminous (A/CNK = 0.98–1.02), and low abundances of phosphorus, titanium, magnesium, and calcium. It is also characterized by enrichment in Rb, K, Th, and depletion in Nb, Ta, P, Ti, and Sr with moderately to weakly negative europium anomalies (Eu/Eu* = 0.71–0.87). Furthermore, the Qushandao Granite displays lower FeOT/MgO, 104 × Ga/Al, and Zr + Nb + Ce + Y values relative to typical A‐type granites. Therefore, we classify the Qushandao Granite as calc‐alkaline I‐type granite based on a synthesis of geological and geochemical characteristics. The Qushandao Granite shows variable zircon Hf isotopic compositions (εHf(t) = −7.6 to +2.3) and TDM2 model ages of 1.40–0.83 Ga with a mean value of 1.17 Ga. We argue that the Qushandao Granite was most likely generated by mixing of mantle‐derived mafic magma and crust‐derived felsic magma in the lower crust, and that it was formed during post‐collisional extension in the Late Cretaceous, related to the gradually increasing subduction angle of the Paleo‐Pacific plate. [ABSTRACT FROM AUTHOR]

Torrential rainfall predisposes hills to catastrophic landslides resulting in serious damage to life and property. Landslide inventory maps are therefore essential for rapid response and developing disaster mitigation strategies. Manual mapping techniques are laborious and time-consuming, and thus not ideal in rapid response situations. Automated landslide mapping from optical satellite imagery using deep neural networks (DNNs) is becoming popular. However, distinguishing landslides from other changed objects in optical imagery using backbone DNNs alone is difficult. Attention modules have been introduced recently into the architecture of DNNs to address this problem by improving the discriminative ability of DNNs and suppressing noisy backgrounds. This study compares two state-of-the-art attention-boosted deep Siamese neural networks in mapping rainfall-induced landslides in the mountainous Himalayan region of Nepal using Planetscope (PS) satellite imagery. Our findings confirm that attention networks improve the performance of DNNs as they can extract more discriminative features. The Siamese Nested U-Net (SNUNet) produced the best and most coherent landslide inventory map among the methods in the test area, achieving an F1-score of 0.73, which is comparable to other similar studies. Our findings demonstrate a prospect for application of the attention-based DNNs in rapid landslide mapping and disaster mitigation not only for rainfall-triggered landslides but also for earthquake-triggered landslides. [ABSTRACT FROM AUTHOR]

This study appraised and compared the performance of process-based hydrological SWAT (soil and water assessment tool) with a machine learning-based multi-layer perceptron (MLP) models for simulating streamflow in the Upper Indus Basin. The study period ranges from 1998 to 2013, where SWAT and MLP models were calibrated/trained and validated/tested for multiple sites during 1998–2005 and 2006–2013, respectively. The performance of both models was evaluated using nash–sutcliffe efficiency (NSE), coefficient of determination (R2), Percent BIAS (PBIAS), and mean absolute percentage error (MAPE). Results illustrated the relatively poor performance of the SWAT model as compared with the MLP model. NSE, PBIAS, R2, and MAPE for SWAT (MLP) models during calibration ranged from the minimum of 0.81 (0.90), 3.49 (0.02), 0.80 (0.25) and 7.61 (0.01) to the maximum of 0.86 (0.99), 9.84 (0.12), 0.87 (0.99), and 15.71 (0.267), respectively. The poor performance of SWAT compared with MLP might be influenced by several factors, including the selection of sensitive parameters, selection of snow specific sensitive parameters that might not represent actual snow conditions, potential limitations of the SCS-CN method used to simulate streamflow, and lack of SWAT ability to capture the hydropeaking in Indus River sub-basins (at Shatial bridge and Bisham Qila). Based on the robust performance of the MLP model, the current study recommends to develop and assess machine learning models and merging the SWAT model with machine learning models. [ABSTRACT FROM AUTHOR]