Combined satellite altimetry and shipborne gravimetry data processing

The recovery of quantities related to the gravity field (i.e., geoid heights and gravity anomalies) is carried out in a test area of the central Mediterranean Sea using 5' × 5' marine gravity data and satellite altimeter data from the Geodetic Mission (GM) of ERS‐J. The optimal combination of the two heterogeneous data sources is performed using (1) the space‐domain least‐squares collocation (LSC) method, and (2) the frequency‐domain input‐output system theory (IOST). The results derived by these methods agree at the level of 2 cm in terms of standard deviation in the case of the geoid height prediction. The gravity anomaly prediction results by the same methods vary between 2.18 and 2.54 mGal in terms of standard deviation. In all cases, the spectral techniques have a much higher computational efficiency than the collocation procedure. In order to investigate the importance of satellite altimetry for gravity field modeling, a pure gravimetric geoid solution, carried out in a previous study for our lest area by the fast collocation approach (FCOL), is used in comparison with the combined geoid models. The combined solutions give more accurate results, at the level of about 15 cm in terms of standard deviation, than the gravimetric geoid solution, when the geoid heights derived by each method are compared with TOPEX altimeter sea surface heights (SSHs). Moreover, nonisotropic power spectral density functions (PSDs) can be easily used by IOST, while LSC requires isotropic covariance functions. The results show that higher prediction accuracies are always obtained when using a priori nonisotropic information instead of isotropic information.     

Improvement in the Determination of the Marine Geoid by Estimating the Bathymetry from Altimetry and Depth Soundings

Abstract

The contribution of bathymetry to the estimation of gravity field related quantities is investigated in an extended test area in the Mediterranean Sea. The region is located southwest of the island of Crete, Greece, bounded between 33^? ≤ ? ≤ 35^? and 15^? ≤ λ ≤ 25^?. Gravity anomalies from the KMS99 gravity field and shipborne depth soundings are used with a priori statistical characteristics of depths in a least-squares collocation procedure to estimate a new bathymetry model. Two different global bathymetry models, namely JGP95E and Sandwell and Smith V8, are used to derive the depth a priori statistical information, while the estimated model is compared against both the global ones and the shipborne depth soundings to assess whether there is an improvement. Various marine geoid models are estimated using ERS1 and GEOSAT Geodetic Mission altimetry and shipborne gravity data. In that process, the effect of the bathymetry is computed using both the estimated and the original depths through a residual terrain modeling reduction. The TOPEX/Poseidon Sea Surface Heights, known for their high accuracy and precision, and the GEOMED solution for the geoid in the Mediterranean are used as control for the validation of the new geoid models and to assess the improvement that the estimated depths offer to geoid modeling. The results show that the newly estimated bathymetry agrees better (by about 30 to 300 m) with the shipborne depth soundings and provides smoother residual geoid heights and gravity anomalies (by about 8–20%) than those from global models. Finally, the achieved accuracy in geoid modeling ranges between 6 and 10 cm (1σ).     

贵州省喀斯特山区植被恢复的物种选择现状分析

喀斯特山区生态系统的恢复和重建,已经成为决定西南地区环境、经济和社会能否持续发展的关键问题,其中植被的生态恢复无疑是极为基础和重要的环节之一,物种选择又是重中之重．通过互联网搜索、文献阅读和野外实地考察,针对喀斯特分布极广且石漠化最为严重的贵州省,本研究调查和分析了喀斯特山区植被生态恢复过程中所选择的物种分布现状,对其恢复效果进行了初步评价,希望能对西南喀斯特山区的生态恢复,尤其是对即将全面铺开的石漠化综合治理提供参考和建议．结果表明：在贵州省78个岩溶县（市、区）的328个地点,共调查收集到用于植被恢复的物种87种,其中生态林物种26种（主要是松、杉、柏、香椿、杨树和车桑子等）,经济林物种47种,包括26种药用植物（太子参、天麻等）和其他21种经济林物种（茶、油茶、梨、桃、李、板栗等）,另外有牧草14种（白花三叶草、黑麦草、紫花苜蓿等）．这些物种聚集或零散种植在不同的喀斯特石漠化综合治理大区：A区（黔西高原山地）、B区（黔中丘原山地）、C区（黔东低山丘陵）和D区（无石漠化）．就恢复效果而言,油茶、茶、中药材、花卉栽培及草地畜牧业均显示出了规模化、产业化发展的良好势头,板栗、核桃、花椒、桃、梨等经济林物种也在其生长适宜地取得了良好的生态和经济效益．然而,植被生态恢复中所营造的人工林,均存在树种单一、物种配置简单、经营粗放等问题,林分的整体功能和生态、经济效益较低,亟待进行改造;不同喀斯特石漠化综合治理区域也需要采取不同的物种配置措施．     

Gauss-Helmert模型及其在测量平差中的应用

对于Gauss-Helmert模型,本文直接从广义逆阵的性质而不必通过Lagrange乘子方法简洁地给出参数向量的最佳线性无偏估计(BLUE),然后将所得结果用于求解测量平差中的拟合推估问题,以及广义的含未知量的带权条件平差问题。     

Fast spherical collocation: theory and examples

 It has long been known that a spherical harmonic analysis of gridded (and noisy) data on a sphere (with uniform error for a fixed latitude) gives rise to simple systems of equations. This idea has been generalized for the method of least-squares collocation, when using an isotropic covariance function or reproducing kernel. The data only need to be at the same altitude and of the same kind for each latitude. This permits, for example, the combination of gravity data at the surface of the Earth and data at satellite altitude, when the orbit is circular. Suppose that data are associated with the points of a grid with N values in latitude and M values in longitude. The latitudes do not need to be spaced uniformly. Also suppose that it is required to determine the spherical harmonic coefficients to a maximal degree and order K. Then the method will require that we solve K systems of equations each having a symmetric positive definite matrix of only N × N. Results of simulation studies using the method are described. Received: 18 October 2001 / Accepted: 4 October 2002 Correspondence to: F. Sansò     

Point Mass Method Applied to the Regional Gravimetric Determination of the Geoid

The determination of the local gravity field by means of the point mass inversion method can be performed as an alternative to conventional numerical methods, such as the least-squares collocation. Based on the first derivative of the inverse-distance Newtonian potential for the representation of the gravity anomaly data, it is possible to compute any wavelength component of the geoid in planar approximation with sufficient accuracy. In order to exemplify the theoretical concept, two applications are presented of the computation of two different wavelength components of the geoid, the long wavelength component in a local solution and the short wavelength component in a regional solution. The results are compared with corresponding least-squares collocation solutions, using a global geopotential model to remove and to restore the long wavelength component.     

ANDALUSGeoid2002: The New Gravimetric Geoid Model of Andalusia (Southern Spain)

In 1991 the first determination of a gravimetric geoid in a test area in central Spain was computed by using least square collocation. In 1995 a gravimetric geoid in the Iberian Peninsula, Ibergeo95, was calculated by FFT. Nowadays an improved geoid of Andalusia, ANDALUSGeoid2002, has been computed by fast collocation procedure and remove-restore technique in the GRS80 Reference System. The computations have been done from 16562 free-air gravity anomaly data set, obtained from IGN (Instituto Geográfico Nacional) and BGI (International Gravity Bureau), the Earth Gravity Model EGM96 and detailed (100 m × 100 m), coarse (5 km × 5 km) and reference (20 km × 20 km) digital terrain models. Relative carrier-phase GPS measurements at 69 benchmarks of the Spanish Levelling Network in Andalusia have been done. The standard deviations of differences between ANDALUSGeoid2002 and GPS/levelling undulations after fitting the tilt have been ± 11 cm, ± 39 cm and ± 38 cm in western, eastern and whole Andalusia, respectively. The ANDALUSGeoid2002 shows an improvement of Ibergeo95 in this territory.     

Modelling local GPS/levelling geoid undulations using artificial neural networks

The use of GPS for establishing height control in an area where levelling data are available can involve the so-called GPS/levelling technique. Modelling of the GPS/levelling geoid undulations has usually been carried out using polynomial surface fitting, least-squares collocation (LSC) and finite-element methods. Artificial neural networks (ANNs) have recently been used for many investigations, and proven to be effective in solving complex problems represented by noisy and missing data. In this study, a feed-forward ANN structure, learning the characteristics of the training data through the back-propagation algorithm, is employed to model the local GPS/levelling geoid surface. The GPS/levelling geoid undulations for Istanbul, Turkey, were estimated from GPS and precise levelling measurements obtained during a field study in the period 1998–99. The results are compared to those produced by two well-known conventional methods, namely polynomial fitting and LSC, in terms of root mean square error (RMSE) that ranged from 3.97 to 5.73 cm. The results show that ANNs can produce results that are comparable to polynomial fitting and LSC. The main advantage of the ANN-based surfaces seems to be the low deviations from the GPS/levelling data surface, which is particularly important for distorted levelling networks.