Contributions from oceanic and ionospheric dynamos to the lunar daily variation at Alibag

Summary. The contributions from the oceanic and ionospheric dynamos, L_o and L_I respectively, to the geomagnetic lunar daily variation, L , at Alibag, a coastal station in the Indian equatorial region, are calculated from the L harmonics derived from a 41–yr long series of observations. The analysis in the calendar months shows a steady and significant ocean dynamo contribution in the vertical component, Z, in all the months except April. Examination, by an analysis of the data year by year, of the association of L_o and L_I with varying solar and magnetic activities reveals, surprisingly, a stable correlation between the magnetic activity index A _P and the oceanic part in the horizontal and vertical components but not in declination, which probably indicates the influence of induced currents, along the latitudes, on L _o.     

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     

Soil characterization based on land cover heterogeneity over a tropical landscape: an integrated approach using earth observation data-sets

Soil is a vital part of the natural environment and is always responding to changes in environmental factors, along with the influences of anthropogenic factors and land use changes. The long-term change in soil properties will result in change in soil health and fertility, and hence the soil productivity. Hence, the main aim of this paper focuses on the analysis of land use/land cover (LULC) change pattern in spatial and temporal perspective and to present its impact on soil properties in the Merawu catchment over the period of 18?years. Post classification change detection was performed to quantify the decadal changes in historical LULC over the periods of 1991, 2001 and 2009. The pixel to pixel comparison method was used to detect the LULC of the area. The key LULC types were selected for investigation of soil properties. Soil samples were analysed in situ to measure the physicochemical soil properties. The results of this study show remarkable changes in LULC in the period of 18?years. The effect of land cover change on soil properties, soil compaction and soil strength was found to be significant at a level of <0.05.     

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.     

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.     

顾及多个因素的城市建成区用地规模预测研究——以郑州市为例

目前,城市建成区用地规模受社会、经济、城市环境和政策等多因素影响,传统统计方法已经难以准确预测城市用地规模,从多因素的角度研究城市用地是合理的。BP神经网络法和多元回归分析法都是顾及多个因素的统计方法。以郑州市1984—2005年相关统计数据为样本数据,用BP神经网络法、多元回归分析法、灰色系统GM(1,1)法和Logistic法建立预测模型,对2006年和2007年建成区规模进行模拟预测。预测结果表明,顾及多个因素的预测方法预测精度较高,其中BP神经网络法优于多元回归分析法。     

Land use/land cover dynamics in Umngot watershed of Meghalaya using geospatial tools

Meghalaya is a landlocked state where the progress of industrialization is very slow. Majority of the population (rural) depends on agriculture. Agriculture in the state is carried on in primitive ways with Jhumming prevailing in many parts. This practice is considered destructive as vast forest areas are cleared and burnt. Such areas are used for cultivation and left out. After a gap of few years (2–3 years as of now and 15–20 years in past) the area is revisited for cultivation without allowing to rejuvenate. The present work provides a status and trend of Land use land cover dynamics in parts (at watershed level) of Meghalaya. The geospatial tools have been used to assess (a) the changes in land use land cover since 1970s to 2005; (b) patch dynamics for understanding the degree of fragmentation; (c) changes along the terrain topography. Umngot watershed of Meghalaya is taken as experimental area, which is a representative of mid-altitudinal watershed particularly in the Jaintia hills district of Meghalaya.