A Long-Term Decrease of the Mid-Size Segmentation Lengths Observed in the He <Emphasis Type="SmallCaps">ii</Emphasis> (30.4 nm) Solar EUV Emission

Power spectra of segmentation-cell length (a dominant length scale of EUV emission in the transition region) from full-disk He?ii extreme ultraviolet (EUV) images observed by the Extreme ultraviolet Imaging Telescope (EIT) onboard the Solar and Heliospheric Observatory (SOHO) and the Atmospheric Imaging Assembly (AIA) onboard the Solar Dynamics Observatory (SDO) during periods of quiet-Sun conditions for a time interval from 1996 to 2015 were analyzed. The spatial power as a function of the spatial frequency from about 0.04 to 0.27 (EIT) or up to 0.48 (AIA) Mm^?1 depends on the distribution of the observed segmentation-cell dimensions – a structure of the solar EUV network. The temporal variations of the spatial power reported by Didkovsky and Gurman (Solar Phys. 289, 153, 2014) were suggested as decreases at the mid-spatial frequencies for the compared spectra when the power curves at the highest spatial frequencies of 0.5 pix^?1 were adjusted to match each other. This approach has been extended in this work to compare spectral ratios at high spatial frequencies expressed in the solar spatial frequency units of Mm^?1. A model of EIT and AIA spatial responses allowed us to directly compare spatial spectral ratios at high spatial frequencies for five years of joint operation of EIT and AIA, from 2010 to 2015. Based on this approach, we represent these ratio changes as a long-term network transformation that may be interpreted as a continuous dissipation of mid-size network structures to the smaller-size structures in the transition region. In contrast to expected cycling of the segmentation-cell dimension structures and associated spatial power in the spectra with the solar cycle, the spectra demonstrate a significant and steady change of the EUV network. The temporal trend across these structural spectra is not critically sensitive to any long-term instrumental changes, e.g. degradation of sensitivity, but to the change of the segmentation-cell dimensions of the EUV network structure.     

Climatic and geographic factors affect ecosystem multifunctionality through biodiversity in the Tibetan alpine grasslands

Ecosystem multifunctionality(EMF), the simultaneous provision of multiple ecosystem functions, is often affected by biodiversity and environmental factors. We know little about how the interactions between biodiversity and environmental factors affect EMF. In this case study, a structural equation model was used to clarify climatic and geographic pathways that affect EMF by varying biodiversity in the Tibetan alpine grasslands. In addition to services related to carbon, nitrogen, and water cycling, forage supply, which is related to plantproductivity and palatability, was included in the EMF index. The results showed that 72% of the variation in EMF could be explained by biodiversity and other environmental factors. The ratio of palatable richness to all species richness explained 8.3% of the EMF variation. We found that air temperature, elevation, and latitude all affected EMF, but in different ways. Air temperature and elevation impacted the aboveground parts of the ecosystem, which included plant height, aboveground biomass, richness of palatable species, and ratio of palatable richness to all species richness. Latitude affected EMF by varying both aboveground and belowground parts of the ecosystem, which included palatable speciesrichness and belowground biomass. Our results indicated that there are still uncertainties in the biodiversity–EMF relationships related to the variable components of EMF, and climatic and geographic factors. Clarification of pathways that affect EMF using structural equation modeling techniques could elucidate the mechanisms by which environmental changes affect EMF.     

Automated mapping of rock slope geometry,kinematics and stability with RSS-GIS

A GIS-implemented, deterministic approach for the automated spatial evaluation of geometrical and kinematical properties of rock slope terrains is presented. Based on spatially distributed directional information on planar geological fabrics and DEM-derived topographic attribute data, the internal geometry of rock slopes can be characterized on a grid cell basis. For such computations, different approaches for the analysis and regionalization of available structural directional information applicable in specific tectonic settings are demonstrated and implemented in a GIS environment. Simple kinematical testing procedures based on feasibility criteria can be conducted on a pixel basis to determine which failure mechanisms are likely to occur at particular terrain locations. In combination with hydraulic and strength data on geological discontinuities, scenario-based rock slope stability evaluations can be performed. For conceptual investigations on rock slope failure processes, a GIS-based specification tool for a 2-D distinct element code (UDEC) was designed to operate with the GIS-encoded spatially distributed rock slope data. The concepts of the proposed methodology for rock slope hazard assessments are demonstrated at three different test sites in Germany.     

The determination of potential difference by the joint application of measured and synthetical gravity data: a case study in Hungary

In an elementary approach every geometrical height difference between the staff points of a levelling line should have a corresponding average g value for the determination of potential difference in the Earth’s gravity field. In practice this condition requires as many gravity data as the number of staff points if linear variation of g is assumed between them. Because of the expensive fieldwork, the necessary data should be supplied from different sources. This study proposes an alternative solution, which is proved at a test bed located in the Mecsek Mountains, Southwest Hungary, where a detailed gravity survey, as dense as the staff point density (~1 point/34 m), is available along a 4.3-km-long levelling line. In the first part of the paper the effect of point density of gravity data on the accuracy of potential difference is investigated. The average g value is simply derived from two neighbouring g measurements along the levelling line, which are incrementally decimated in the consecutive turns of processing. The results show that the error of the potential difference between the endpoints of the line exceeds 0.1 mm in terms of length unit if the sampling distance is greater than 2 km. Thereafter, a suitable method for the densification of the decimated g measurements is provided. It is based on forward gravity modelling utilising a high-resolution digital terrain model, the normal gravity and the complete Bouguer anomalies. The test shows that the error is only in the order of 10⁻³mm even if the sampling distance of g measurements is 4 km. As a component of the error sources of levelling, the ambiguity of the levelled height difference which is the Euclidean distance between the inclined equipotential surfaces is also investigated. Although its effect accumulated along the test line is almost zero, it reaches 0.15 mm in a 1-km-long intermediate section of the line.     

Using gravity and topography-implied anomalies to assess data requirements for precise geoid computation

Many regions around the world require improved gravimetric data bases to support very accurate geoid modeling for the modernization of height systems using GPS. We present a simple yet effective method to assess gravity data requirements, particularly the necessary resolution, for a desired precision in geoid computation. The approach is based on simulating high-resolution gravimetry using a topography-correlated model that is adjusted to be consistent with an existing network of gravity data. Analysis of these adjusted, simulated data through Stokes’s integral indicates where existing gravity data must be supplemented by new surveys in order to achieve an acceptable level of omission error in the geoid undulation. The simulated model can equally be used to analyze commission error, as well as model error and data inconsistencies to a limited extent. The proposed method is applied to South Korea and shows clearly where existing gravity data are too scarce for precise geoid computation.     

A hierarchical model for estimating methane emission from wetlands using MODIS data and ARIMA modeling

A three-step hierarchical Semi Automated Empirical Methane Emission Model (SEMEM) has been used to estimate methane emission from wetlands and waterlogged areas in India using Moderate Resolution Imagine Spectroradiometer (MODIS) sensor data onboard Terra satellite. Wetland Surface Temperature (WST), methane emission fluxes and wetland extent have been incorporated as parameters in order to model the methane emission. Analysis of monthly MODIS data covering the whole of India from November 2004 to April 2006 was carried out and monthly methane emissions have been estimated. Interpolation techniques were adopted to fill the data gaps due to cloudy conditions during the monsoon period. AutoRegressive Integrated Moving Average (ARIMA) model has been fitted to estimate the emitted methane for the months of May 2006 to August 2006 using SPSS software.     

Spatial-Correlation Based Persistent Scatterer Interferometric Study for Ground Deformation

Interferometric Synthetic Aperture Radar (InSAR), nowadays, is a precise technique for monitoring and detecting ground deformation at a millimetric level over large areas using multi-temporal SAR images. Persistent Scatterer Interferometric SAR (PSInSAR), an advanced version of InSAR, is an effective tool for measuring ground deformation using temporally stable reference points or persistent scatterers. We have applied both PSInSAR and Small Baseline Subset (SBAS) methods, based on the spatial correlation of interferometric phase, to estimate the ground deformation and time-series analysis. In this study, we select Las Vegas, Nevada, USA as our test area to detect the ground deformation along satellite line-of-sight (LOS) during November 1992–September 2000 using 44 C-band SAR images of the European Remote Sensing (ERS-1 and ERS-2) satellites. We observe the ground displacement rate of Las Vegas is in the range of ?19 to 8 mm/year in the same period. We also cross-compare PSInSAR and SBAS using mean LOS velocity and time-series. The comparison shows a correlation coefficient of 0.9467 in the case of mean LOS velocity. Along this study, we validate the ground deformation results from the satellite with the ground water depth of Las Vegas using time-series analysis, and the InSAR measurements show similar patterns with ground water data.     

Ionospheric and receiver DCB-constrained multi-GNSS single-frequency PPP integrated with MEMS inertial measurements

Single-frequency precise point positioning (SF-PPP) is a potential precise positioning technique due to the advantages of the high accuracy in positioning after convergence and the low cost in operation. However, there are still challenges limiting its applications at present, such as the long convergence time, the low reliability, and the poor satellite availability and continuity in kinematic applications. In recent years, the achievements in the dual-frequency PPP have confirmed that its performance can be significantly enhanced by employing the slant ionospheric delay and receiver differential code bias (DCB) constraint model, and the multi-constellation Global Navigation Satellite Systems (GNSS) data. Accordingly, we introduce the slant ionospheric delay and receiver DCB constraint model, and the multi-GNSS data in SF-PPP modular together. In order to further overcome the drawbacks of SF-PPP in terms of reliability, continuity, and accuracy in the signal easily blocking environments, the inertial measurements are also adopted in this paper. Finally, we form a new approach to tightly integrate the multi-GNSS single-frequency observations and inertial measurements together to ameliorate the performance of the ionospheric delay and receiver DCB-constrained SF-PPP. In such model, the inter-system bias between each two GNSS systems, the inter-frequency bias between each two GLONASS frequencies, the hardware errors of the inertial sensors, the slant ionospheric delays of each user-satellite pair, and the receiver DCB are estimated together with other parameters in a unique Kalman filter. To demonstrate its performance, the multi-GNSS and low-cost inertial data from a land-borne experiment are analyzed. The results indicate that visible positioning improvements in terms of accuracy, continuity, and reliability can be achieved in both open-sky and complex conditions while using the proposed model in this study compared to the conventional GPS SF-PPP.     

A two-step ionospheric modeling algorithm considering the impact of GLONASS pseudo-range inter-channel biases

The Global Navigation Satellite System presents a plausible and cost-effective way of computing the total electron content (TEC). But TEC estimated value could be seriously affected by the differential code biases (DCB) of frequency-dependent satellites and receivers. Unlike GPS and other satellite systems, GLONASS adopts a frequency-division multiplexing access mode to distinguish different satellites. This strategy leads to different wavelengths and inter-frequency biases (IFBs) for both pseudo-range and carrier phase observations, whose impacts are rarely considered in ionospheric modeling. We obtained observations from four groups of co-stations to analyze the characteristics of the GLONASS receiver P1P2 pseudo-range IFB with a double-difference method. The results showed that the GLONASS P1P2 pseudo-range IFB remained stable for a period of time and could catch up to several meters, which cannot be absorbed by the receiver DCB during ionospheric modeling. Given the characteristics of the GLONASS P1P2 pseudo-range IFB, we proposed a two-step ionosphere modeling method with the priori IFB information. The experimental analysis showed that the new algorithm can effectively eliminate the adverse effects on ionospheric model and hardware delay parameters estimation in different space environments. During high solar activity period, compared to the traditional GPS + GLONASS modeling algorithm, the absolute average deviation of TEC decreased from 2.17 to 2.07 TECu (TEC unit); simultaneously, the average RMS of GPS satellite DCB decreased from 0.225 to 0.219 ns, and the average deviation of GLONASS satellite DCB decreased from 0.253 to 0.113 ns with a great improvement in over 55%.     

Tropospheric refractivity and zenith path delays from least-squares collocation of meteorological and GNSS data

Precise positioning requires an accurate a priori troposphere model to enhance the solution quality. Several empirical models are available, but they may not properly characterize the state of troposphere, especially in severe weather conditions. Another possible solution is to use regional troposphere models based on real-time or near-real time measurements. In this study, we present the total refractivity and zenith total delay (ZTD) models based on a numerical weather prediction (NWP) model, Global Navigation Satellite System (GNSS) data and ground-based meteorological observations. We reconstruct the total refractivity profiles over the western part of Switzerland and the total refractivity profiles as well as ZTDs over Poland using the least-squares collocation software COMEDIE (Collocation of Meteorological Data for Interpretation and Estimation of Tropospheric Pathdelays) developed at ETH Zürich. In these two case studies, profiles of the total refractivity and ZTDs are calculated from different data sets. For Switzerland, the data set with the best agreement with the reference radiosonde (RS) measurements is the combination of ground-based meteorological observations and GNSS ZTDs. Introducing the horizontal gradients does not improve the vertical interpolation, and results in slightly larger biases and standard deviations. For Poland, the data set based on meteorological parameters from the NWP Weather Research and Forecasting (WRF) model and from a combination of the NWP model and GNSS ZTDs shows the best agreement with the reference RS data. In terms of ZTD, the combined NWP-GNSS observations and GNSS-only data set exhibit the best accuracy with an average bias (from all stations) of 3.7 mm and average standard deviations of 17.0 mm w.r.t. the reference GNSS stations.