http://www.mdpi.com/journal/remotesensing/special_issues/gps-and-applications/ {snippet name="submission_info"} http://www.mdpi.com/2072-4292/3/4/668/ The determination of the orthometric height from geometric leveling has practical difficulties that, despite a number of scientific and technological advances, passed a century without substantial modifications or advances. Currently, the Global Navigation Satellite System (GNSS) has been used with reasonable success for orthometric height determination. With a sufficient number of benchmarks with known horizontal and vertical coordinates, it is often possible to adjust using the least squares method mathematical expressions that allow interpolation of geoid heights. The objective of this study is to present an alternative method to interpolate geoid heights based on the technique of Artificial Neural Networks (ANNs). The study area is the Brazilian state of São Paulo, and for training the ANN the authors have used geoid height information from the EGM08 gravity model with a grid spacing of 10 minutes of arc. The efficiency of the model was tested at 157 points with known geoid heights distributed across the study area. The results were also compared with the Brazilian Geoid Model (MAPGEO2004). Based on those 157 benchmarks it was possible to verify that the model generated by ANNs provided a mean absolute error of 0.24 m in obtaining a geoid height value. Statistical tests have shown that there was no difference between the means from known geoid heights and geoid heights provided by the neural model for a significance level of 5%. It was also found that ANNs provided an improvement of 2.7 times in geoid height estimates when compared with the MAPGEO2004 geoid model. http://www.mdpi.com/2072-4292/3/4/668/ Wed, 30 Mar 2011 00:00:00 CEST Remote Sensing 2011-03-30 3 4 Article 668 683 2072-4292 Regional Mapping of the Geoid Using GNSS (GPS) Measurements and an Artificial Neural Network 2011-03-30 doi: 10.3390/rs3040668 Mauricio Roberto Veronez Sérgio Florêncio de Souza Marcelo Tomio Matsuoka Alessandro Reinhardt Reginaldo Macedônio da Silva http://www.mdpi.com/2072-4292/3/3/435/ In Washington State, USA, mountain goats (Oreamnos americanus) have experienced a long-term population decline. To assist management, we created annual and seasonal (summer and winter) habitat models based on 2 years of data collected from 38 GPS-collared (GPS plus collar v6, Vectronic-Aerospace GmbH, Berlin, Germany) mountain goats in the western Cascades. To address GPS bias of position acquisition, we evaluated habitat and physiographic effects on GPS collar performance at 543 sites in the Cascades. In the western Cascades, total vegetation cover and the quadratic mean diameter of trees were shown to effect GPS performance. In the eastern Cascades, aspect and total vegetation cover were found to influence GPS performance. To evaluate the influence of bias correction on the analysis of habitat selection, we created resource selection functions with and without bias correction for mountain goats in the western Cascades. We examined how well the resultant habitat models performed with reserved data (25% of fixes from 38 study animals) and with data from 9 other GPS-collared mountain goats that were both temporally and spatially independent. The statistical properties of our GPS bias correction model were similar to those previously reported explaining between 20 and 30% of the variation, however, application of bias correction improved the accuracy of the mountain goat habitat model by only 1–2% on average and did not alter parameter estimates in a meaningful, or consistent manner. Despite statistical limitations, our habitat models, most notably during the winter, provided the widest extent and most detailed models of the distribution of mountain goat habitat in the Cascades yet developed. http://www.mdpi.com/2072-4292/3/3/435/ Mon, 28 Feb 2011 00:00:00 CET Remote Sensing 2011-02-28 3 3 Article 435 459 2072-4292 GPS Bias Correction and Habitat Selection by Mountain Goats 2011-02-28 doi: 10.3390/rs3030435 Adam G. Wells David O. Wallin Clifford G. Rice Wan-Ying Chang http://www.mdpi.com/2072-4292/2/10/2426/ A nonlinear least squares fitting algorithm is used to estimate both snow depth and snow density for a snow-layer above a flat ground reflector. The product of these two quantities, snow depth and density, provides an estimate of the snow water equivalent. The input to this algorithm is a simple ray model that includes a speculary reflected signal along with a direct signal. These signals are transmitted from the global positioning system satellites at 1.57542 GHz with right-hand circularly polarization. The elevation angles of interest at the GPS receiving antenna are between 5° and 30°. The results from this nonlinear algorithm show potential for inferring snow water equivalent using GPS multipath signals. http://www.mdpi.com/2072-4292/2/10/2426/ Wed, 20 Oct 2010 00:00:00 CEST Remote Sensing 2010-10-20 2 10 Article 2426 2441 2072-4292 Inferring Snow Water Equivalent for a Snow-Covered Ground Reflector Using GPS Multipath Signals 2010-10-20 doi: 10.3390/rs2102426 Mark D. Jacobson http://www.mdpi.com/2072-4292/2/8/2017/ This paper presents two space detected Global Positioning System (GPS)signals reflected off sea ice and compares the returned power profiles with independent estimates of ice concentration provided by the Advanced Microwave Scanning Radiometer (AMSR-E) and sea ice charts from the National Ice Center. The results of the analysis show significantly different signals received for each of the GPS reflections. For the first collection,comparisons with ice concentration estimates from AMSR-E and the National Ice Centers reveal a very strong GPS signal return off high concentration sea ice. The second GPS data collection occurs over a region of changing sea ice concentration, and the GPS signal level responds at roughly the same point that the AMSR-E data and National Ice Center charts indicate a change in ice concentration. However, the very strong signal of the first GPS collection is not consistent in magnitude with similar ice concentrations during the secondGPS data collection. This demonstration shows the potential and the difficulties of this new technique as a valuable low-cost compliment to existing sea ice monitoring instruments. Additionally, a general method for calculating the location of the specular reflection point on the Earth’s surface and the received Doppler frequencies and code phase delays is presented as part of an on-board open-loop signal tracking technique. http://www.mdpi.com/2072-4292/2/8/2017/ Wed, 25 Aug 2010 00:00:00 CEST Remote Sensing 2010-08-25 2 8 Article 2017 2039 2072-4292 Towards Sea Ice Remote Sensing with Space Detected GPS Signals: Demonstration of Technical Feasibility and Initial Consistency Check Using Low Resolution Sea Ice Information 2010-08-25 doi: 10.3390/rs2082017 Scott Gleason http://www.mdpi.com/2072-4292/2/5/1320/ In this study, we compare specific humidity profiles derived from Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC) radio occultation (RO) from August to November 2006 with those from different types of radiosonde and from ECMWF global analysis. Comparisons show that COSMIC specific humidity data agree well with ECMWF analysis over different regions of the world for both day and night times. On the contrary, evaluation against COSMIC specific humidity shows a distinct dry bias of Shang-E radiosonde (China) and an obvious wet bias of VIZ-type (USA). No obvious specific humidity biases are found for MRZ (Russia) and MEISEI (Japan) radiosondes. These results demonstrate the usefulness of COSMIC water vapor for quantifying the dry/wet biases among different sensor types. http://www.mdpi.com/2072-4292/2/5/1320/ Fri, 07 May 2010 00:00:00 CEST Remote Sensing 2010-05-07 2 5 Article 1320 1330 2072-4292 Global Evaluation of Radiosonde Water Vapor Systematic Biases using GPS Radio Occultation from COSMIC and ECMWF Analysis 2010-05-07 doi: 10.3390/rs2051320 Ho Zhou Kuo Hunt Wang