Degradation monitoring in Silvo-pastoral systems: A case study of the Mediterranean region of Turkey

To identify policies that will promote positive effects and mitigate negative ones of grazing is a major challenge in the Silvo-pastoral system. This paper presents the role of examining land-cover change trajectories by remote sensing imagery in grazing policy monitoring. The study was conducted for Duzlercami forest ecosystem located in the Mediterranean geographical region of Turkey and administrated by the General Directorate of Forestry (GDF) of the Ministry of Forestry and Water Affairs. Time series land-cover datasets from Landsat images between 1988 and 2016 were collected and classified. To link the conversions among trajectories and grazing policy, class level landscape metrics derived from the classified images were used. To validate the approach, yearly grazing-plans managed by GDF and populations of livestock were used. Results of this research have indicated that even though there is a yearly grazing plan, overgrazing can happen on the pilot site, and it can be easily identified by the destruction of woody vegetation. The notable correlation (r²?=?0.89) between degraded woody vegetation and cattle population has occurred in the last 30?years in the landscape, and Landsat imagery can effectively support the grazing policy mapping and monitoring.     

Potential of IRNSS/NavIC L5 signals for ionospheric studies

Indian Space Research Organization (ISRO) has developed an indigenous system named Indian Regional Navigation Satellite System (IRNSS) or NavIC (Navigation with Indian Constellation), that consists of 7 satellites and transmits navigation signal in L and S bands. ISRO, for validation of the system, has installed many IGS (IRNSS/GPS/SBAS) receivers scattered over the Indian region. Using preliminary data from two geographically widely separated stations over India, this paper presents the results on studies on parameters of IRNSS signal quality and discusses how these parameters may be used to study the ionospheric behavior over the Indian region. The results show the importance and advantages of using IRNSS data for such studies.     

Time-series analysis of GPS measurements for long-span bridge movements using wavelet and model prediction techniques

This study aims at assessing the safety behavior of the Incheon long-span bridge using high rate (10?Hz) geodetic monitoring global positioning system (GPS). The time series of wavelet spectrum analysis is utilized to assess the dynamic behavior of the bridge. The coefficients and model errors of the time series autoregressive-moving average (ARMA) model are used to evaluate the movement performances of the bridge. The results show that: (i) the accuracy of GPS measurements to extract the dynamic behavior of the bridge is 97.27% when compared with the design results. (ii) the behavior of the bridge is within the safety limits of the bridge design with minimum observed changes for the historical GPS measurements in time and frequency domains, the mean deflection of bridge deck is 8.26?mm and frequency changes of bridge is 0.004?Hz compared with the design results. (iii) the time series analysis of the wavelet spectrum and ARMA model coefficients can be used to detect the significant frequency changes and study the rigidity of the bridge performance, respectively; and the both methods are found to be suitable techniques to estimate the performance changes of the GPS measurements in the time and frequency domains during the monitoring time period.     

Evaluation and improvement of coastal GNSS reflectometry sea level variations from existing GNSS stations in Taiwan

Global sea level rise due to an increasingly warmer climate has begun to induce hazards, adversely affecting the lives and properties of people residing in low-lying coastal regions and islands. Therefore, it is important to monitor and understand variations in coastal sea level covering offshore regions. Signal-to-noise ratio (SNR) data of Global Navigation Satellite System (GNSS) have been successfully used to robustly derive sea level heights (SLHs). In Taiwan, there are a number of continuously operating GNSS stations, not originally installed for sea level monitoring. They were established in harbors or near coastal regions for monitoring land motion. This study utilizes existing SNR data from three GNSS stations (Kaohsiung, Suao, and TaiCOAST) in Taiwan to compute SLHs with two methods, namely, Lomb–Scargle Periodogram (LSP)-only, and LSP aided with tidal harmonic analysis developed in this study. The results of both methods are compared with co-located or nearby tide gauge records. Due to the poor quality of SNR data, the worst accuracy of SLHs derived from traditional LSP-only method exceeds 1?m at the TaiCOAST station. With our procedure, the standard deviations (STDs) of difference between GNSS-derived SLHs and tide gauge records in Kaohsiung and Suao stations decreased to 10?cm and the results show excellent agreement with tide gauge derived relative sea level records, with STD of differences of 7?cm and correlation coefficient of 0.96. In addition, the absolute GNSS-R sea level trend in Kaohsiung during 2006–2011 agrees well with that derived from satellite altimetry. We conclude that the coastal GNSS stations in Taiwan have the potential of monitoring absolute coastal sea level change accurately when our proposed methodology is used.     

Physical Calibrations of the FREND Instrument Installed Onboard TGO Martian Orbiter

Cosmic Research - The article presents results of ground calibrations of the FREND neutron telescope installed onboard the TGO spacecraft of the Russian-European ExoMars project. The main goal of...     

Spectral Characteristics of Self-Emission from Electric Thrusters with Closed Electron Drift in the Radio-Frequency Band for Various Propellants

Cosmic Research - We have considered procedure and results of the experimental study for spectral characteristics of self emission from laboratory models of electric thrusters with closed electron...     

ExoMars 2016 Schiaparelli Module Trajectory and Atmospheric Profiles Reconstruction

On 19^th October 2016 Schiaparelli module of the ExoMars 2016 mission flew through the Mars atmosphere. After successful entry and descent under parachute, the module failed the last part of the descent and crashed on the Mars surface. Nevertheless the data transmitted in real-time by Schiaparelli during the entry and descent, together with the entry state vector as initial condition, have been used to reconstruct both the trajectory and the profiles of atmospheric density, pressure and temperature along the traversed path.The available data-set is only a small sub-set of the whole data acquired by Schiaparelli, with a limited data rate (8 kbps) and a large gap during the entry because of the plasma blackout on the communications.This paper presents the work done by the AMELIA (Atmospheric Mars Entry and Landing Investigations and Analysis) team in the exploitation of the available inertial and radar data. First a reference trajectory is derived by direct integration of the inertial measurements and a strategy to overcome the entry data gap is proposed. First-order covariance analysis is used to estimate the uncertainties on all the derived parameters. Then a refined trajectory is computed incorporating the measurements provided by the on-board radar altimeter.The derived trajectory is consistent with the events reported in the telemetry and also with the impact point identified on the high-resolution images of the landing site.Finally, atmospheric profiles are computed tacking into account the aerodynamic properties of the module. Derived profiles result in good agreement with both atmospheric models and available remote sensing observations.