Dunes on Titan observed by Cassini Radar

Thousands of longitudinal dunes have recently been discovered by the Titan Radar Mapper on the surface of Titan. These are found mainly within ±30° of the equator in optically-, near-infrared-, and radar-dark regions, indicating a strong proportion of organics, and cover well over 5% of Titan's surface. Their longitudinal duneform, interactions with topography, and correlation with other aeolian forms indicate a single, dominant wind direction aligned with the dune axis plus lesser, off-axis or seasonally alternating winds. Global compilations of dune orientations reveal the mean wind direction is dominantly eastwards, with regional and local variations where winds are diverted around topographically high features, such as mountain blocks or broad landforms. Global winds may carry sediments from high latitude regions to equatorial regions, where relatively drier conditions prevail, and the particles are reworked into dunes, perhaps on timescales of thousands to tens of thousands of years. On Titan, adequate sediment supply, sufficient wind, and the absence of sediment carriage and trapping by fluids are the dominant factors in the presence of dunes.     

Elastic anisotropy of ferromagnesian post-perovskite in Earth's D” layer

We report experimental observation of a sizable elastic anisotropy in a polycrystalline sample of ferromagnesian silicate in post-perovskite (ppv) structure. Using a novel composite X-ray transparent gasket to contain and synthesize ppv in a panoramic diamond-anvil cell along with oblique X-ray diffraction geometry, we observed the anisotropic lattice strain and {1 0 0} or {1 1 0} slip-plane texture in the sample at 140 GPa. We deduced the elasticity tensor (c_ij), orientation-dependent compressional wave velocities, polarization-dependent shear-wave velocities, and the velocity anisotropy of the silicate ppv. Our results are consistent with calculations and indicate that with sufficient preferred orientation, the elastic anisotropy of this phase can produce large shear-wave splitting.     

Sea‐level and ocean‐current control on carbonate‐platform growth,Maldives, Indian Ocean

Multichannel high‐resolution seismic and multibeam data were acquired from the Maldives‐isolated carbonate platform in the Indian Ocean for a detailed characterization of the Neogene bank architecture of this edifice. The goal of the research is to decipher the controlling factors of platform evolution, with a special emphasis on sea‐level changes and changes of the oceanic currents. The stacking pattern of Lower to Middle Miocene depositional sequences, with an evolution of a ramp geometry to a flat‐topped platform, reflects variations of accommodation, which here are proposed to be primarily governed by fluctuations of relative sea level. Easterly currents during this stage of bank growth controlled an asymmetric east‐directed progradation of the bank edge. During the late middle Miocene, this system was replaced by a twofold configuration of bank development. Bank growth continued synchronously with partial bank demise and associated sediment‐drift deposition. This turnover is attributed to the onset and/or intensification of the Indian monsoon and related upwelling and occurrence of currents, locally changing environmental conditions and impinging upon the carbonate system. Mega spill over lobes, shaped by reversing currents, formed as large‐scale prograding complexes, which have previously been interpreted as deposits formed during a forced regression. On a regional scale, a complex carbonate‐platform growth can occur, with a coexistence of bank‐margin progradation and aggradation, as well as partial drowning. It is further shown that a downward shift of clinoforms and offlapping geometries in carbonate platforms are not necessarily indicative for a sea‐level driven forced regression. Findings are expected to be applicable to other examples of Cenozoic platforms in the Indo‐Pacific region.     

Airborne gravimetry used in precise geoid computations by ring integration

Two detailed geoids have been computed in the region of North Jutland. The first computation used marine data in the offshore areas. For the second computation the marine data set was replaced by the sparser airborne gravity data resulting from the AGMASCO campaign of September 1996. The results of comparisons of the geoid heights at on-shore geometric control showed that the geoid heights computed from the airborne gravity data matched in precision those computed using the marine data, supporting the view that airborne techniques have enormous potential for mapping those unsurveyed areas between the land-based data and the off-shore marine or altimetrically derived data. Received: 7 July 1997 / Accepted: 22 April 1998     

军民测绘标准融合方法研究

为了统筹军地资源,加强国防建设和经济建设,国家对军民融合的发展战略进行了统一部署。军民测绘标准融合是推动军民测绘事业发展的一项重要内容。通过梳理和对比分析现有军民测绘标准体系及其标准之间的相互关系,提出军民测绘标准融合的工作建议,从而为军民测绘标准融合工作的开展与实施提供参考。     

Earth Observation Scientific Workflows in a Distributed Computing Environment

Geospatially Enabled Scientific Workflows offer a promising toolset to help researchers in the earth observation domain with many aspects of the scientific process. One such aspect is that of access to distributed earth observation data and computing resources. Earth observation research often utilizes large datasets requiring extensive CPU and memory resources in their processing. These resource intensive processes can be chained; the sequence of processes (and their provenance) makes up a scientific workflow. Despite the exponential growth in capacity of desktop computers, their resources are often insufficient for the scientific workflow processing tasks at hand. By integrating distributed computing capabilities into a geospatially enabled scientific workflow environment, it is possible to provide researchers with a mechanism to overcome the limitations of the desktop computer. Most of the effort on extending scientific workflows with distributed computing capabilities has focused on the web services approach, as exemplified by the OGC's Web Processing Service and by GRID computing. The approach to leveraging distributed computing resources described in this article uses instead remote objects via RPyC and the dynamic properties of the Python programming language. The Vistrails environment has been extended to allow for geospatial processing through the EO4Vistrails package ( http://code.google.com/p/eo4vistrails/ ). In order to allow these geospatial processes to be seamlessly executed on distributed resources such as cloud computing nodes, the Vistrails environment has been extended with both multi‐tasking capabilities and distributed processing capabilities. The multi‐tasking capabilities are required in order to allow Vistrails to run side‐by‐side processes, a capability it does not currently have. The distributed processing capabilities are achieved through the use of remote objects and mobile code through RPyC.     

DMSP-OLS与NPP-VIIRS夜间灯光遥感影像数据整合

DMSP-OLS夜间灯光遥感数据截至2013年,现已被NPP-VIIRS夜间灯光数据取代。因此,要获得长时间序列且稳定的夜间灯光数据集,需要整合两类夜间灯光数据。基于此,本文提出了基于重采样的两类数据整合方法,对2013—2020年NPP-VIIRS数据进行模拟,最终建立了1992—2020年长时间序列校正—模拟DMSP-OLS夜光遥感数据集。结果表明,基于重采样的整合方法效果良好(城市区域Pearson相关系数ρ=0.9852,RMSE=3.4607),整合数据集与相关社会经济参考量高度契合(影像DN值总和与GDP的相关系数ρ=0.946,与人口的相关系数ρ=0.971,二次多项式模型拟合R²≈0.98,RMSE<5.55),优于已有研究。因此,利用该方法整合后的数据集能更好地支撑基于夜间灯光影像的长时间序列研究。     

A Composite Semisupervised SVM for Classification of Hyperspectral Images

This letter presents a novel composite semisupervised support vector machine (SVM) for the spectral-spatial classification of hyperspectral images. In particular, the proposed technique exploits the following: 1) unlabeled data for increasing the reliability of the training phase when few training samples are available and 2) composite kernel functions for simultaneously taking into account spectral and spatial information included in the considered image. Experiments carried out on a hyperspectral image pointed out the effectiveness of the presented technique, which resulted in a significant increase of the classification accuracy with respect to both supervised SVMs and progressive semisupervised SVMs with single kernels, as well as supervised SVMs with composite kernels.