Estimation of Above Ground Biomass for Central Indian Deciduous Forests Using ALOS PALSAR L-Band Data

Reliable and accurate estimates of tropical forest above ground biomass (AGB) are important to reduce uncertainties in carbon budgeting. In the present study we estimated AGB of central Indian deciduous forests of Madhya Pradesh (M.P.) state, India, using Advanced Land Observing Satellite – Phased Array type L-band Synthetic Aperture Radar (ALOS-PALSAR) L-band data of year 2010 in conjunction with field based AGB estimates using empirical models. Digital numbers of gridded 1?×?1° dual polarization (HH & HV) PALSAR mosaics for the study area were converted to normalized radar cross section (sigma naught - σ⁰). A total of 415 sampling plots (0.1 ha) data collected over the study area during 2009–10 was used in the present study. Plot-level AGB estimates using volume equations representative to the study area were computed using field inventory data. The plot-level AGB estimates were empirically modeled with the PALSAR backscatter information in HH, HV and their ratios from different forest types of the study area. The HV backscatter information showed better relation with field based AGB estimates with a coefficient of determination (R²) of 0.509 which was used to estimate spatial AGB of the study area. Results suggested a total AGB of 367.4 Mt for forests of M.P. state. Further, validation of the model was carried out using observed vs. predicted AGB estimates, which suggested a root mean square error (RMSE) of ±19.32 t/ha. The model reported robust and defensible relation for observed vs. predicted AGB values of the study area.     

基于GPS无验潮水深测量在自然河段冲淤变化的精度探讨

无验潮模式已经在相关领域得到成熟的运用,但是在内河用于水文泥沙冲淤分析尚属空白。本文简要介绍了GPS无验潮测量的基本原理及作业方法,结合2014年长江三峡工程库区重庆市主城区河段河道演变观测项目,以九龙坡典型河段为例,对该河段进行了验潮与无验潮模式测量,并对两种方法得出的结果进行了计算与分析。测试结果表明,基于GPS无验潮测量模式进行水文泥沙项目观测,对水文泥沙分析精度有一定的影响,对勘测项目的经济效益上有一定的提高。     

土地利用现状和规划数据建库容差浅析

通过对第二次全国土地调查(以下简称"二调")和土地利用总体规划数据建库过程中误差的产生和传播分析,通过误差理论和容差设置实际研究。提出现行数据建库容许误差(以下简称容差)设置不合理,设置过高引起大量无用的数据处理工作。建议合理设置容差参数,提高数据库建设和更新效率。     

TEC variation over an equatorial and anomaly crest region in India during 2012 and 2013

Using the global positioning system (GPS) measurements, the total electron content (TEC) at station Bangalore (13.02°N, 77.57°E geographic; 04.44°N, 150.84°E geomagnetic), lying at the equatorial region, and station Lucknow (26.91°N, 80.95°E geographic; 17.96°N, 155.24°E geomagnetic), lying at equatorial ionospheric anomaly (EIA) crest region, have been estimated for the year 2012–2013. In order to evaluate the International Reference Ionosphere (IRI) model regarding simulation/modeling of ionospheric studies specially at equatorial and EIA crest regions, we have compared the TEC derived from the recent version of the IRI-2012 model and the older IRI-2007 with its three topside options, namely IRI-NeQuick (IRI-NeQ), IRI-2001, and IRI01-corr, with that of GPS-TEC over Bangalore and Lucknow. For the EIA station Lucknow, the IRI-2012 model with IRI-NeQ and IRI01-corr topside is found in good agreement with GPS-TEC during summer and equinox season, while the IRI-2012 model for all three topside options significantly overestimates the GPS-TEC during winter season. The IRI-2001 topside overestimates the GPS-TEC over both the stations during all seasons. The anomalous difference between the IRI-2012 model prediction and ground-based GPS-TEC in daytime hours during the winter season observed at Lucknow could be attributed to discrepancies in the slab thickness predicted by the model, which is more during the winter season as compared to summer and equinox. These large discrepancies in the slab thickness predicted by the IRI-2012 as well as the IRI-2007 model during the winter season have been supported by using the foF2 data from Constellation Observing System for Meteorology, Ionosphere, and Climate radio occultation-based measurements. We also observed that the discrepancies in the recent IRI-2012 model with respect to GPS-TEC are found to be slightly larger than those with the older IRI-2007 model over the EIA region Lucknow. However, over the equatorial region Bangalore, the discrepancy with the older model IRI-2007 was found to be larger than with the recent IRI-2012 model. This suggests that the performance of the IRI-2012 model is poorer than the IRI-2007 model at the EIA region while better at equatorial region, and that further improvements in the IRI-2012 models are required particularly in the low-latitude and EIA regions. The GPS-TEC showed disappearance of the winter anomaly during 2012–2013, while the IRI model failed to predict the disappearance of winter anomaly.     

Long-term soil moisture dynamics derived from GNSS interferometric reflectometry: a case study for Sutherland,South Africa

Soil moisture is a geophysical key observable for predicting floods and droughts, modeling weather and climate and optimizing agricultural management. Currently available in situ observations are limited to small sampling volumes and restricted number of sites, whereas measurements from satellites lack spatial resolution. Global navigation satellite system (GNSS) receivers can be used to estimate soil moisture time series at an intermediate scale of about 1000 m². In this study, GNSS signal-to-noise ratio (SNR) data at the station Sutherland, South Africa, are used to estimate soil moisture variations during 2008–2014. The results capture the wetting and drying cycles in response to rainfall. The GNSS Volumetric Water Content (VWC) is highly correlated (r ² = 0.8) with in situ observations by time-domain reflectometry sensors and is accurate to 0.05 m³/m³. The soil moisture estimates derived from the SNR of the L1 and L2P signals compared to the L2C show small differences with a RMSE of 0.03 m³/m³. A reduction in the SNR sampling rate from 1 to 30 s has very little impact on the accuracy of the soil moisture estimates (RMSE of the VWC difference 1–30 s is 0.01 m³/m³). The results show that the existing data of the global tracking network with continuous observations of the L1 and L2P signals with a 30-s sampling rate over the last two decades can provide valuable complementary soil moisture observations worldwide.     

Using a regional numerical weather prediction model for GNSS positioning over Brazil

The global navigation satellite system (GNSS) can provide centimeter positioning accuracy at low costs. However, in order to obtain the desired high accuracy, it is necessary to use high-quality atmospheric models. We focus on the troposphere, which is an important topic of research in Brazil where the tropospheric characteristics are unique, both spatially and temporally. There are dry regions, which lie mainly in the central part of the country. However, the most interesting area for the investigation of tropospheric models is the wet region which is located in the Amazon forest. This region substantially affects the variability of humidity over other regions of Brazil. It provides a large quantity of water vapor through the humidity convergence zone, especially for the southeast region. The interconnection and large fluxes of water vapor can generate serious deficiencies in tropospheric modeling. The CPTEC/INPE (Center for Weather Forecasting and Climate Studies/Brazilian Institute for Space Research) has been providing since July 2012 a numerical weather prediction (NWP) model for South America, known as Eta. It has yield excellent results in weather prediction but has not been used in GNSS positioning. This NWP model was evaluated in precise point positioning (PPP) and network-based positioning. Concerning PPP, the best positioning results were obtained for the station SAGA, located in Amazon region. Using the NWP model, the 3D RMS are less than 10 cm for all 24 h of data, whereas the values reach approximately 60 cm for the Hopfield model. For network-based positioning, the best results were obtained mainly when the tropospheric characteristics are critical, in which case an improvement of up to 7.2 % was obtained in 3D RMS using NWP models.     

Generating GPS satellite fractional cycle bias for ambiguity-fixed precise point positioning

With the development of precise point positioning (PPP), the School of Geodesy and Geomatics (SGG) at Wuhan University is now routinely producing GPS satellite fractional cycle bias (FCB) products with open access for worldwide PPP users to conduct ambiguity-fixed PPP solution. We provide a brief theoretical background of PPP and present the strategies and models to compute the FCB products. The practical realization of the two-step (wide-lane and narrow-lane) FCB estimation scheme is described in detail. With GPS measurements taken in various situations, i.e., static, dynamic, and on low earth orbit (LEO) satellites, the quality of FCB estimation and the effectiveness of PPP ambiguity resolution (AR) are evaluated. The comparison with CNES FCBs indicated that our FCBs had a good consistency with the CNES ones. For wide-lane FCB, almost all the differences of the two products were within ±0.05 cycles. For narrow-lane FCB, 87.8 % of the differences were located between ±0.05 cycles, and 97.4 % of them were located between ±0.075 cycles. The experimental results showed that, compared with conventional ambiguity-float PPP, the averaged position RMS of static PPP can be improved from (3.6, 1.4, 3.6) to (2.0, 1.0, 2.7) centimeters for ambiguity-fixed PPP. The average accuracy improvement in the east, north, and up components reached 44.4, 28.6, and 25.0 %, respectively. A kinematic, ambiguity-fixed PPP test with observation of 80 min achieved a position accuracy of better than 5 cm at the one-sigma level in all three coordinate components. Compared with the results of ambiguity-float, kinematic PPP, the positioning biases of ambiguity-fixed PPP were improved by about 78.2, 20.8, and 65.1 % in east, north, and up. The RMS of LEO PPP test was improved by about 23.0, 37.0, and 43.0 % for GRACE-A and GRACE-B in radial, tangential, and normal directions when AR was applied to the same data set. These results demonstrated that the SGG FCB products can be produced with high quality for users anywhere around the world to carry out ambiguity-fixed PPP solutions.     

A closed-loop EKF and multi-failure diagnosis approach for cooperative GNSS positioning

Current cooperative positioning with global navigation satellite system (GNSS) for connected vehicle application mainly uses pseudorange measurements. However, the positioning accuracy offered cannot meet the requirements for lane-level positioning, collision avoidance and future automatic driving, which needs real-time positioning accuracy of better than 0.5 m. Furthermore, there is an apparent lack of research into the integrity issue for these new applications under emerging driverless vehicle applications. In order to overcome those problems, a new extended Kalman filter (EKF) and a multi-failure diagnosis algorithm are developed to process both GNSS pseudorange and carrier phase measurements. We first introduce a new closed-loop EKF with partial ambiguity resolution as feedback to address the low accuracy issue. Then a multi-failure diagnosis algorithm is proposed to improve integrity and reliability. The core of this new algorithm includes using Carrier phase-based Receiver Autonomous Integrity Monitoring method for failure detection, and the double extended w test detectors to identify failure. A cooperative positioning experiment was carried out to validate the proposed method. The results show that the proposed closed-loop EKF can provide highly accurate positioning, and the multi-failure diagnosis method is effective in detecting and identifying failures for both code and carrier phase measurements.     

Modified compressive sensing approach for GNSS signal reception in the presence of interference

Pre-processing traditional navigation signals in global navigation satellite system (GNSS) receivers includes the conversion of an analog-to-digital sample and acquisition following the basic principle of Nyquist sampling theory. This condition inevitably increases the system computation time and cost of a modern wideband receiver. In recent years, the compressive sensing (CS) approach has been proven to effectively reduce the number of measurement samples required for digital signal acquisition systems. This method gives new potential to this modern design. In this study, a modified compressive sensing algorithm for the acquisition of a GNSS signal that is contaminated by an interfering signal is presented. The proposed method attempts to combine CS demodulation and the subspace projecting method to enhance GNSS signal acquisition performance with interference present. First, the received signal is sub-sampled and aliased from a compressive sampling process. This operation maintains the restricted isometry property (RIP) condition of the second sampling process using a Toeplitz-structured sensing matrix, which replaces a conventional random sensing matrix. The matrix ensures that distances between desired signals on the set of sparse space are not influenced by the sampling process. Next, the interference is eliminated through the orthogonal feature between the interference signal and the desired signal using the subspace projecting method. This also preserves the RIP of the projecting matrix to ensure that the original structure of the linear projection of the signal is preserved. After this, an iterative least-square method is utilized to recover the correlator output from the reception samples taken by the CS demodulator. In addition, the signal detection performance in the presence of co-channel interference using a CS demodulator is analyzed and evaluated. Finally, the relationships between signal detection probability, compressive factor and signal bandwidth are also illustrated. Several numerical results are presented to verify the theory.     

Weak GPS signal tracking using FFT discriminator in open loop receiver

An open loop tracking architecture, which tracks GPS signals under weak and challenging conditions, is analyzed. The in-phase and quadrature-phase integration pair is regarded as a single tone complex signal. An FFT-based method is used as a frequency discriminator to estimate the Doppler frequency residual of the single tone signal. Another FFT-based method applies complex squaring to eliminate the effect of the navigation data bits polarities. The performance of the FFT-based discriminators is assessed in three criteria. Those criteria are the signal strength and dynamic range that can be tracked and the accuracy of the estimated Doppler frequency. In addition, the performance of the discriminators is analyzed to provide the theoretical and simulated peak detection probability. The results indicate that the FFT discriminator can track signals about 5 dB weaker than the signals that can be tracked by the complex squared FFT discriminator. In a quasi-static environment, the Doppler frequency residual can be assumed to be around zero, which can enable the FFT-based discriminators to track signals with approximately 2 dB less power. Moreover, the performance of the FFT-based discriminators is compared with the performance of two other frequency discriminators, namely the fast–slow and power-based. The comparison results indicate that these two frequency discriminators give higher frequency estimation accuracy, but they have a narrower dynamic range.