道路交通空间数据WebGIS发布技术探讨

介绍道路交通空间数据WebGIS发布技术研究的必要性。在介绍WebGIS的功能特点的基础上,简要介绍了超图软件SuperMapIS．NET的特性。最后,详细论述了运用SuperMapIS．NET发布交通数据的步骤,并做了简要数据地图的发布实现。     

Hyperspectral remote sensing of evaporate minerals and associated sediments in Lake Magadi area, Kenya

Pleistocene to present evaporitic lacustrine sediments in Lake Magadi, East African Rift Valley, Kenya were studied and mapped using spectral remote sensing methods. This approach incorporated surface mineral mapping using space-borne hyperspectral Hyperion imagery together with laboratory analysis, including visible, near-infrared diffuse reflectance spectroscopy (VNIR) measurements and X-ray diffraction for selected rock and soil samples of the study area. The spectral signatures of Magadiite and Kenyaite, which have not been previously reported, were established and the spectral signatures of trona, chert series, volcanic tuff and the High Magadi bed were also analyzed.Image processing techniques, MNF (Minimum Noise Fraction) and MTMF (Mixture Tuned Matched Filtering) using a stratified approach (image analysis with and without the lake area), were used to enhance the mapping of evaporates. High Magadi beds, chert series and volcanic tuff were identified from the Hyperion image with an overall mapping accuracy of 84.3%. Even though, the spatial distribution of evaporites and sediments in Lake Magadi area change in response to climate variations, the mineralogy of this area has not been mapped recently. The results of this study shows the usefulness of the hypersspectral remote sensing to map the surface geology of this kind of environment and to locate promising sites for industrial open-pit trona mining in a qualitative and quantitative manner.     

Modelling and Remote Sensing of Land Surface Temperature in Turkey

This study introduces artificial neural networks (ANNs) for the estimation of land surface temperature (LST) using meteorological and geographical data in Turkey (26?C45°E and 36?C42°N). A generalized regression neural network (GRNN) was used in the network. In order to train the neural network, meteorological and geographical data for the period from January 2002 to December 2002 for 10 stations (Adana, Afyon, Ankara, Eski?ehir, ?stanbul, ?zmir, Konya, Malatya, Rize, Sivas) spread over Turkey were used as training (six stations) and testing (four stations) data. Latitude, longitude, elevation and mean air temperature are used in the input layer of the network. Land surface temperature is the output. However, land surface temperature has been estimated as monthly mean by using NOAA-AVHRR satellite data in the thermal range over 10 stations in Turkey. The RMSE between the estimated and ground values for monthly mean with ANN temperature(LST_ANN) and Becker and Li temperature(LST_B-L) method values have been found as 0.077?K and 0.091?K (training stations), 0.045?K and 0.003?K (testing stations), respectively.     

Nonlinear Adjustment of GPS Observations of Type Pseudo-Ranges

The nonlinear adjustment of GPS observations of type pseudo-ranges is performed in two steps. In step one a combinatorial minimal subset of observations is constructed which is rigorously converted into station coordinates by means of Groebner basis algorithm or the multipolynomial resultant algorithm. The combinatorial solution points in a polyhedron are reduced to their barycentric in step two by means of their weighted mean. Such a weighted mean of the polyhedron points in ℝ³ is generated via the Error Propagation law/variance-covariance propagation. The Fast Nonlinear Adjustment Algorithm (FNon Ad Al) has been already proposed by Gauss whose work was published posthumously and Jacobi (1841). The algorithm, here referred to as the Gauss-Jacobi Combinatorial algorithm, solves the over-determined GPS pseudo-ranging problem without reverting to iterative or linearization procedure except for the second moment (Variance-Covariance propagation). The results compared well with the solutions obtained using the linearized least squares approach giving legitimacy to the Gauss-Jacobi combinatorial procedure. ? 2002 Wiley Periodicals, Inc.     

Hedonic price models with omitted variables and measurement errors: a constrained autoregression–structural equation modeling approach with application to urban Indonesia

Omitted variables and measurement errors in explanatory variables frequently occur in hedonic price models. Ignoring these problems leads to biased estimators. In this paper, we develop a constrained autoregression–structural equation model (ASEM) to handle both types of problems. Standard panel data models to handle omitted variables bias are based on the assumption that the omitted variables are time-invariant. ASEM allows handling of both time-varying and time-invariant omitted variables by constrained autoregression. In the case of measurement error, standard approaches require additional external information which is usually difficult to obtain. ASEM exploits the fact that panel data are repeatedly measured which allows decomposing the variance of a variable into the true variance and the variance due to measurement error. We apply ASEM to estimate a hedonic housing model for urban Indonesia. To get insight into the consequences of measurement error and omitted variables, we compare the ASEM estimates with the outcomes of (1) a standard SEM, which does not account for omitted variables, (2) a constrained autoregression model, which does not account for measurement error, and (3) a fixed effects hedonic model, which ignores measurement error and time-varying omitted variables. The differences between the ASEM estimates and the outcomes of the three alternative approaches are substantial.     

Land Cover Mapping for Namdapha National Park (Arunachal Pradesh), India Using Harmonized Land Cover Legends

Since last few decades RS-GIS is playing vital role in studying and mapping spatiotemporal responses of land cover, however, as a matter of fact, the mapping outputs largely depend on the expert's/user's preferences because location specific and people specific land cover classification systems are adopted autonomously for image classification in GIS. This may actually lead to an ambiguous definition of a particular land cover type when such different maps are compared at global level. In 1993, FAO and UNEP started efforts for development of a software tool know as LCCS which is a comprehensive standardized tool capable of providing land cover characterization to all possible land cover types in the world regardless of spatial relevance, mapping scale, data collection method etc. Adding to the global efforts of land cover legend harmonization and mapping, this study presents development of harmonized land cover legends for Namdapha National Park located in north-eastern Indian Himalayan region using LCCS and subsequent mapping. The potential of Remote Sensing (RS) and Geographical Information Systems (GIS) in forest/land cover mapping is very well recognized. Therefore, adopting the developed harmonized legends for the study area, land cover mapping was done using RS-GIS approach.     

Multifrequency algorithms for precise point positioning: MAP3

We present the new MAP3 algorithms to perform static precise point positioning (PPP) from multifrequency and multisystem GNSS observations. MAP3 represents a two-step strategy in which the least squares theory is applied twice to estimate smoothed pseudo-distances, initial phase ambiguities, and slant ionospheric delay first, and the absolute receiver position and its clock offset in a second adjustment. Unlike the classic PPP technique, in our new approach, the ionospheric-free linear combination is not used. The combination of signals from different satellite systems is accomplished by taking into account the receiver inter-system bias. MAP3 has been implemented in MATLAB and integrated within a complete PPP software developed on site and named PCube. We test the MAP3 performance numerically and contrast it with other external PPP programs. In general, MAP3 positioning accuracy with low-noise GPS dual-frequency observations is about 2.5 cm in 2-h observation periods, 1 cm in 10 h, and 7 mm after 1 day. This means an improvement in the accuracy in short observation periods of at least 7 mm with respect to the other PPP programs. The MAP3 convergence time is also analyzed and some results obtained from real triple-frequency GPS and GIOVE observations are presented.     

Non-linear motions of Australian geodetic stations induced by non-tidal ocean loading and the passage of tropical cyclones

We investigate daily and sub-daily non-tidal oceanic and atmospheric loading (NTOAL) in the Australian region and put an upper bound on potential site motion examining the effects of tropical cyclone Yasi that crossed the Australian coast in January/February 2011. The dynamic nature of the ocean is important, particularly for northern Australia where the long-term scatter due to daily and sub-daily oceanic changes increases by 20–55 % compared to that estimated using the inverted barometer (IB) assumption. Correcting the daily Global Positioning System (GPS) time series for NTOAL employing either a dynamic ocean model or the IB assumption leads to a reduction of up to 52 % in the weighted scatter of daily coordinate estimates. Differences between the approaches are obscured by seasonal variations in the GPS precision along the northern coast. Two compensating signals during the cyclone require modelling at high spatial and temporal resolution: uplift induced by the atmospheric depression, and subsidence induced by storm surge. The latter dominates ( \(>\) 135 %) the combined net effect that reaches a maximum of 14 mm, and 10 mm near the closest GPS site TOW2. Here, 96 % of the displacement is reached within 15 h due to the rapid transit of cyclones and the quasi-linear nature of the coastline. Consequently, estimating sub-daily NTOAL is necessary to properly account for such a signal that can be 3.5 times larger than its daily-averaged value. We were unable to detect the deformation signal in 2-hourly GPS processing and show that seasonal noise in the Austral summer dominates and precludes GPS detection of the cyclone-related subsidence.