机载光阵探头探测期间云粒子的破碎

机载光阵探头探测时,云粒子(液态和固态)进入二维光阵探头的采样区前,会因与探头探测臂发生机械碰撞,或者与探头外壳产生的湍流和风切变相互作用而破碎。破碎程度与粒子类型、大小、粒子密度、探头入口设计以及飞行空速等有关。利用2008年7~9月探测飞机(Y-12)在山西省太原地区的航测资料并对飞机采样期间的云粒子破碎现象进行介绍和分析,分析结果表明,粒子到达时间间隔分布具有双模态特征:长时间模态是粒子空间分布的真实结构,短时间模态则是云粒子破碎的结果。提出用粒子到达时间间隔阈值作为粒子破碎的判定标准,给出适用于2008年太原地区航测资料的粒子破碎识别阈值,其中适合于探头云粒子成像仪(CIP)的阈值是2×10-5 s,而探头降水粒子成像仪(PIP)的阈值是1×10-4 s。所提的阈值对于以Y-12为机载探测平台,以CIP和PIP探头为探测仪器所获取的其它航次云微物理图像资料的粒子破碎处理也是有一定的参考使用价值。     

Estimating dissolved organic carbon concentration in turbid coastal waters using optical remote sensing observations

Dissolved Organic Carbon (DOC) is an important component in the global carbon cycle. It also plays an important role in influencing the coastal ocean biogeochemical (BGC) cycles and light environment. Studies focussing on DOC dynamics in coastal waters are data constrained due to the high costs associated with in situ water sampling campaigns. Satellite optical remote sensing has the potential to provide continuous, cost-effective DOC estimates. In this study we used a bio-optics dataset collected in turbid coastal waters of Moreton Bay (MB), Australia, during 2011 to develop a remote sensing algorithm to estimate DOC. This dataset includes data from flood and non-flood conditions. In MB, DOC concentration varied over a wide range (20–520 μM C) and had a good correlation (R² = 0.78) with absorption due to coloured dissolved organic matter (CDOM) and remote sensing reflectance. Using this data set we developed an empirical algorithm to derive DOC concentrations from the ratio of R_rs(412)/R_rs(488) and tested it with independent datasets. In this study, we demonstrate the ability to estimate DOC using remotely sensed optical observations in turbid coastal waters.     

光学后向散射法在海洋现场探测无机悬浮颗粒物质的应用研究

本文论述了使用作者于１９８８年研制成功的国内第一台拖曳荧光计系统，通过改换荧光测量滤片为中性滤光片。     

福建3个港湾和闽南—台湾浅滩海域光学参数的研究

本文根据现场浮游植物光合作用速率和叶缘素a 的测定值,以及海水光强度的衰减变化,计算了海水消光系数,推导了福建东山湾、罗源湾、厦门西港和闽南-台湾浅滩海域海水消光系数与透明度的经验关系式;讨论了悬浮颗粒对不同深度下浮游植物生长速率的影响。结果表明,福建近岸海域光学参数的差异主要在于悬浮物质的贡献;不同季节,生物活动对光学参数也有所影响。最大光合作用速率一般出现在光衰减为10%～30%,频率分布因子为70%～80%。     

Simultaneous observations of second and sub-second time structures in H α , radio and hard X-ray data due to the periodical particle acceleration and MHD waves in the November 1, 2004 flare

Second and sub-second structures were simultaneously detected in optical, radio and hard X-ray (HXR) band, respectively by the GanYu Station of Purple Mountain Observatory, Nobeyama Radio Observatory, and RHESSI satellite in the November 1, 2004 flare (Ji et al., in Astrophys. J. 636:L173, 2006), which may be contributed to the energy transport of the continuous heat flux from the hot corona or chromosphere evaporation and of the accelerated electrons. The linear correlations between the amplitudes of these fluctuations and their flare emissions, and those between the cross-correlation coefficients of the fluctuations at two H _α kernels, or two radio frequencies, or two X-ray energies and their flare emissions may support the causal relationship of the flare and these time structures. While, the cross-correlations of the fluctuations at three different bands suggest that the fluctuations are caused by the common thermal or nonthermal processes in the flare. Moreover, some new features of the fluctuations are reported in the flare: (1) The sub-second fluctuations in radio and HXR bands have a same timescale, which is evidently larger than that in H-alpha band. The difference may be explained by the downward movements of nonthermal electrons or the upward motion of chromosphere evaporation. (2) The power-law distributions of the amplitudes of the second and the sub-second structures are obtained at optical, radio and HXR bands with different indices. (3) The peak-to-peak correspondence of Stokes I and V components in the sub-second structures at radio band suggests that they may be resulted from a periodical particle acceleration and particle injection in this event. However, the second structures may be caused by the modulations of Alfvén waves with an upward speed of 10³ km/s.     

Deep neural network for complex open-water wetland mapping using high-resolution WorldView-3 and airborne LiDAR data

Wetland inventory maps are essential information for the conservation and management of natural wetland areas. The classification framework is crucial for successful mapping of complex wetlands, including the model selection, input variables and training procedures. In this context, deep neural network (DNN) is a powerful technique for remote sensing image classification, but this model application for wetland mapping has not been discussed in the previous literature, especially using commercial WorldView-3 data. This study developed a new framework for wetland mapping using DNN algorithm and WorldView-3 image in the Millrace Flats Wildlife Management Area, Iowa, USA. The study area has several wetlands with a variety of shapes and sizes, and the minimum mapping unit was defined as 20 m² (0.002 ha). A set of potential variables was derived from WorldView-3 and auxiliary LiDAR data, and a feature selection procedure using principal components analysis (PCA) was used to identify the most important variables for wetland classification. Furthermore, traditional machine learning methods (support vector machine, random forest and k-nearest neighbor) were also implemented for the comparison of results. In general, the results show that DNN achieved satisfactory results in the study area (overall accuracy = 93.33 %), and we observed a high spatial overlap between reference and classified wetland polygons (Jaccard index ∼0.8). Our results confirm that PCA-based feature selection was effective in the optimization of DNN performance, and vegetation and textural indices were the most informative variables. In addition, the comparison of results indicated that DNN classification achieved relatively similar accuracies to other methods. The total classification errors vary from 0.104 to 0.111 among the methods, and the overlapped areas between reference and classified polygons range between 87.93 and 93.33 %. Finally, the findings of this study have three main implications. First, the integration of DNN model and WorldView-3 image is useful for wetland mapping at 1.2-m, but DNN results did not outperform other methods in this study area. Second, the feature selection was important for model performance, and the combination of most relevant input parameters contributes to the success of all tested models. Third, the spatial resolution of WorldView-3 is appropriate to preserve the shape and extent of small wetlands, while the application of medium resolution image (30-m) has a negative impact on the accurate delineation of these areas. Since commercial satellite data are becoming more affordable for remote sensing users, this study provides a framework that can be utilized to integrate very high-resolution imagery and deep learning in the classification of complex wetland areas.     

Waveform-based point cloud classification in land-cover identification

Full-waveform topographic LiDAR data provide more detailed information about objects along the path of a laser pulse than discrete-return (echo) topographic LiDAR data. Full-waveform topographic LiDAR data consist of a succession of cross-section profiles of landscapes and each waveform can be decomposed into a sum of echoes. The echo number reveals critical information in classifying land cover types. Most land covers contain one echo, whereas topographic LiDAR data in trees and roof edges contained multi-echo waveform features. To identify land-cover types, waveform-based classifier was integrated single-echo and multi-echo classifiers for point cloud classification.The experimental area was the Namasha district of Southern Taiwan, and the land-cover objects were categorized as roads, trees (canopy), grass (grass and crop), bare (bare ground), and buildings (buildings and roof edges). Waveform features were analyzed with respect to the single- and multi-echo laser-path samples, and the critical waveform features were selected according to the Bhattacharyya distance. Next, waveform-based classifiers were performed using support vector machine (SVM) with the local, spatial features of waveform topographic LiDAR information, and optical image information. Results showed that by using fused waveform and optical information, the waveform-based classifiers achieved the highest overall accuracy in identifying land-cover point clouds among the models, especially when compared to an echo-based classifier.     

地球静止卫星和网格化站点支持下的PM2.5精细制图

许多城市建立的相对稠密的网格化监测站点,为精细化监管城市空气质量奠定了基础。本文选用徐州市网格化监测数据、地球静止卫星Himawari-8/AHI及COMS/GOCI的表观反射率和气溶胶光学厚度数据、气象和其他辅助数据,开展了徐州地区0.005°空间分辨率网格的PM_2.5浓度精细化制图研究。本文使用了极端梯度提升（XGBoost）、随机森林（RF）及时空加权回归（GTWR）等3种方法,并选用多种特征参数组合进行对比分析。综合分析模型精度和过拟合程度,结果表明XGBoost模型表现最好,其R²为0.90,RMSE为11.65 μg/m³。进一步将本文结果与国控站点、清华大学的TAP数据集和马里兰大学的CHAP数据集的对比分析,结果表明基于网格化站点的PM_2.5制图结果能更好地反映城市内部不同区域的PM_2.5浓度分布差异性,弥补因国控站点稀疏带来的缺陷,更好地服务于城市空气质量精准管控。     

Altimetry and transponder ground simulation experiment

We have designed and built a compact demonstrator unit for the investigation of altimetry and transponder applications. A small light-weight breadboard carries a compact frequency doubled Nd:YAG pulse laser, an afocal beam expansion optics, a small receiver telescope with spectral and spatial filter arrangements and a sensitive photo-detector. The output laser energy can be as high as 45 mJ with a pulse-width of 3 ns and the telescope aperture is 10 cm. Simulations [Degnan, J.J., 2006. Simulating interplanetary transponder and laser communications experiments via dual station ranging to SLR satellites. In: Proceedings of the 15th International Workshop on Laser Ranging, Canberra, Australia, pp. 457-462] suggested that the link margin for low Earth orbiting satellites (LEO) is comfortable. Successful satellite laser ranging from this dual-station experiment in several different configurations was achieved up to the MEO orbit in a 10 day provisional installation.     

An economical optical cloud/fog detector

A new, relatively low cost instrument has been developed to detect the presence of fog or cloud for fog/cloud sampling applications. The instrument uses attenuation of an 880 nm light emitting diode signal to detect cloud/fog drops in the optical path between a sending and receiving arm. Laboratory and field testing under a variety of conditions and fog types were carried out to determine the ability of the optical fog detector (OFD) to accurately detect cloud/fog presence as well as to provide some measure of liquid water content. Results indicated that the OFD provided a reliable estimate of fog presence as well as a reasonable estimate of liquid water content (LWC) under several different conditions. The OFD does appear to have an interference from rain, resulting in an overestimation of LWC during rainfall. This may occasionally give a false positive indication of fog presence.