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In an elementary approach every geometrical height difference between the staff points of a levelling line should have a corresponding
average g value for the determination of potential difference in the Earth’s gravity field. In practice this condition requires as
many gravity data as the number of staff points if linear variation of g is assumed between them. Because of the expensive fieldwork, the necessary data should be supplied from different sources.
This study proposes an alternative solution, which is proved at a test bed located in the Mecsek Mountains, Southwest Hungary,
where a detailed gravity survey, as dense as the staff point density (~1 point/34 m), is available along a 4.3-km-long levelling
line. In the first part of the paper the effect of point density of gravity data on the accuracy of potential difference is
investigated. The average g value is simply derived from two neighbouring g measurements along the levelling line, which are incrementally decimated in the consecutive turns of processing. The results
show that the error of the potential difference between the endpoints of the line exceeds 0.1 mm in terms of length unit if
the sampling distance is greater than 2 km. Thereafter, a suitable method for the densification of the decimated g measurements is provided. It is based on forward gravity modelling utilising a high-resolution digital terrain model, the
normal gravity and the complete Bouguer anomalies. The test shows that the error is only in the order of 10−3mm even if the sampling distance of g measurements is 4 km. As a component of the error sources of levelling, the ambiguity of the levelled height difference which
is the Euclidean distance between the inclined equipotential surfaces is also investigated. Although its effect accumulated
along the test line is almost zero, it reaches 0.15 mm in a 1-km-long intermediate section of the line. 相似文献
77.
Many regions around the world require improved gravimetric data bases to support very accurate geoid modeling for the modernization
of height systems using GPS. We present a simple yet effective method to assess gravity data requirements, particularly the
necessary resolution, for a desired precision in geoid computation. The approach is based on simulating high-resolution gravimetry
using a topography-correlated model that is adjusted to be consistent with an existing network of gravity data. Analysis of
these adjusted, simulated data through Stokes’s integral indicates where existing gravity data must be supplemented by new
surveys in order to achieve an acceptable level of omission error in the geoid undulation. The simulated model can equally
be used to analyze commission error, as well as model error and data inconsistencies to a limited extent. The proposed method
is applied to South Korea and shows clearly where existing gravity data are too scarce for precise geoid computation. 相似文献
78.
Reshu Agarwal Rakesh Gupta J. K. Garg 《Journal of the Indian Society of Remote Sensing》2009,37(3):473-481
A three-step hierarchical Semi Automated Empirical Methane Emission Model (SEMEM) has been used to estimate methane emission
from wetlands and waterlogged areas in India using Moderate Resolution Imagine Spectroradiometer (MODIS) sensor data onboard
Terra satellite. Wetland Surface Temperature (WST), methane emission fluxes and wetland extent have been incorporated as parameters
in order to model the methane emission. Analysis of monthly MODIS data covering the whole of India from November 2004 to April
2006 was carried out and monthly methane emissions have been estimated. Interpolation techniques were adopted to fill the
data gaps due to cloudy conditions during the monsoon period. AutoRegressive Integrated Moving Average (ARIMA) model has been
fitted to estimate the emitted methane for the months of May 2006 to August 2006 using SPSS software. 相似文献
79.
Yong Ge Sanping Li V. Chris Lakhan Arko Lucieer 《International Journal of Applied Earth Observation and Geoinformation》2009
The existence of uncertainty in classified remotely sensed data necessitates the application of enhanced techniques for identifying and visualizing the various degrees of uncertainty. This paper, therefore, applies the multidimensional graphical data analysis technique of parallel coordinate plots (PCP) to visualize the uncertainty in Landsat Thematic Mapper (TM) data classified by the Maximum Likelihood Classifier (MLC) and Fuzzy C-Means (FCM). The Landsat TM data are from the Yellow River Delta, Shandong Province, China. Image classification with MLC and FCM provides the probability vector and fuzzy membership vector of each pixel. Based on these vectors, the Shannon's entropy (S.E.) of each pixel is calculated. PCPs are then produced for each classification output. The PCP axes denote the posterior probability vector and fuzzy membership vector and two additional axes represent S.E. and the associated degree of uncertainty. The PCPs highlight the distribution of probability values of different land cover types for each pixel, and also reflect the status of pixels with different degrees of uncertainty. Brushing functionality is then added to PCP visualization in order to highlight selected pixels of interest. This not only reduces the visualization uncertainty, but also provides invaluable information on the positional and spectral characteristics of targeted pixels. 相似文献
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