Geophysical Survey

About: Geophysical Survey is a facility organization based out in Obninsk, Russia. It is known for research contribution in the topics: Geology & Seismology. The organization has 308 authors who have published 256 publications receiving 3067 citations. The organization is also known as: Federal State Institution of Science Geophysical Survey of the Siberian Branch of the Russian Academy of Sciences.

The influence analysis of reflectance anisotropy of canopy on the prediction accuracy of Cu stress based on laboratory multi-directional measurement

Abstract: The atmosphere calibrated airborne and space borne hyperspectral images are the HDRF of canopy. The spatial nonuniformity of HDRF may result in inversion errors of the heavy metal stressing. In this paper, the HDRF of copper stressed plant samples under different illumination conditions was acquired with the laboratory hyperspectral simulation system called MHRS2F. The difference between the HDRF of canopy and the BCRF of leaves was firstly discussed. Then the changes of spatial distribution of the HDRF for different copper concentrations and illumination conditions were discussed. At last, the sensitivity of various vegetation indices to illumination and observation directions was compared. By comparing the prediction accuracy of different vegetation indices on different observation directions and illumination conditions, the HVI and mRENDVI were found to be more stable and accurate.

Exceptional middle latitude electron precipitation detected by balloon observations: implications for atmospheric composition

Abstract: Abstract. Energetic particle precipitation leads to ionization in the Earth's atmosphere, initiating the formation of active chemical species which destroy ozone and have the potential to impact atmospheric composition and dynamics down to the troposphere. We report on one exceptionally strong high-energy electron precipitation event detected by balloon measurements in geomagnetic midlatitudes on 14 December 2009, with ionization rates locally comparable to strong solar proton events. This electron precipitation was possibly caused by wave–particle interactions in the slot region between the inner and outer radiation belts, connected with still poorly understood natural phenomena in the magnetosphere. Satellite observations of odd nitrogen and nitric acid are consistent with widespread electron precipitation into magnetic midlatitudes. Simulations with a 3D chemistry–climate model indicate the almost complete destruction of ozone in the upper mesosphere over the region where high-energy electron precipitation occurred. Such an extraordinary type of energetic particle precipitation can have major implications for the atmosphere, and their frequency and strength should be carefully studied.

An improved 1D filtering method for LIDAR point cloud

Abstract: This paper discusses how to separate non-ground points from raw LIDAR point cloud For the purpose of improving processing efficiency and precision, an improved 1-D filtering method is proposed The entire filtering process is divided into eight steps and non-ground points are eliminated progressively In these processing steps, a key-point detection technique is used to segment points in profile Based on thes e profile segments, detailed analysis is utilized to implement segment-oriented filtering innovatively This method makes us e of entire features of segmen tal points for classification, so it is more accuracy and robust than traditional point-by-point classification Two different scale datasets are used to test our method Compared to 1-D labeling method, the proposed method is more effective and efficiency Keywords: LIDAR, 1-D filtering, scan lin e, key point, segmentation INTRODUCTION In the last 10 years, LIDAR technology has been proven as an effective technique for the acquisition of 3D topographic information Combined with a Global Positioning System (GPS) and an Inertial Measur ement Unit (IMU), LIDAR can generate 3D dense, geo-referenced point clouds directly for the reflective terrain surface These point clouds are valuable for several applications For example, LIDAR point clouds can be used to produce DTMs, hydrologic models, 3D urban models, transportation network models, and the like In the most of applications, eliminating non-ground points form original LIDAR footprints is the most important processing technology The process of eliminating non-ground points from the entire LIDAR data to obtain ground points is called LIDAR data filtering (Vosselman, 2000) Many techniques have been developed to deal with points filtering One kind of methods is based on mathematical morphology (Kilian, 1996; Zhang, 2003), points which operated by morphological erosion are compared to original surface to discriminate ground and non-g round points Another grou p of filtering methods is based on the manipulation of Triangulated Irregular Network (Axelsson, P, 1999), ground points are classified through progressive densification of a TIN model The third kind of methods is using hierarchic robust interpolation and surf ace fitting (Kraus and Pfeifer, 1998), terrain surface is fitting by points and iteratively decline to authentic terrain As a lot of filtering methods have been proposed, Sithole and Vosselman (2004 ) compare these methods detailed and test them with standard test datasets Existing algorithms have achieved some success, but due to the high diversity of terrain, the issue of LIDAR points filtering is far from resolved Due to LIDAR points are acquired in scanning mode, discrete points are organized according to scan line Different from the 2D filtering approaches, which process LIDAR points within a 2D neighborhood, 1D filtering methods directly utilize the intrinsic relation of LIDAR points to determine point neighborhood among scan line more quickly J Shan (2005) proposed a one-dimensional (1-D) labeling algorithm to filter LIDAR point This method is computationally efficient, but not always effective In this paper, we propose an improved 1-D filtering algorithm and use two LIDAR datasets to test it Experiments show our algorithm is not only quickly and efficient, bu t also more steady and accurate than the 1-D labeling method

Discussion on the Tectonic Setting of Deposition of the Eocene Sikouzi Formation in Ningxia Region

Abstract: As the first sediment in Cenozoic time in Ningxia region, the Eocene Sikouzi Formation has special tectonic meaning, however, its development has not been interpreted satisfactorily yet. From the point of deposition scope and more radical change of sedimentary facies, the Sikouzi Formation occurred abruptly. By the means of facies and structural analysis, the authors think that the Sikouzi Formation was deposited on the forebulge facing to the Corridor Nan Shan which resulted in the development of one foreland basin 30~40 Ma ago, the forebulge unit of this foreland basin was located along the western margin of Ordos Basin and Xiangshan area, the back-bulge unit lay to the east of western margin of Ordos Basin. Because of flexing, many normal faults dipping to the orogen came into being on the forebulge, it was these normal faults that controlled the distribution of Sikouzi Formation which was deposited rapidly and formed a serial of alluvial fans around forebulge, the provenance of it was the uplifts along the western margin of Ordos Basin and Xiangshan area, etc.