Loading in 2 Seconds...
Loading in 2 Seconds...
Experiments with National Digital Elevation Models Yaron A. Felus, Robert C. Burtch, and Chad Schaeding Surveying Engineering Department Ferris State University, MI. http://btcsure1.ferris.edu/NGA/ 915 Campus Dr. Swan 314, Big-Rapids, MI 49307 E-mail: email@example.com or firstname.lastname@example.org.
Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.
Experiments with National Digital Elevation ModelsYaron A. Felus, Robert C. Burtch, and Chad SchaedingSurveying Engineering DepartmentFerris State University, MI
915 Campus Dr. Swan 314, Big-Rapids, MI 49307
E-mail: email@example.com or firstname.lastname@example.org
February 11, 2000, the Space Shuttle gathered topographic data over approximately 80% of the land surfaces of the Earth.
Class 1 map should have a vertical RMSE of 1/3 the contour interval for well-defined points and 1/6 the contour interval for spot elevations.
Maps compiled within limiting RMSE errors of twice or three times those allowed for Class 1 map shall be designated as Class 2 or Class 3, respectively.
Section 142 of Act No. 59 of the Public Acts of 1978, as amended, being S559.242 of the Michigan Compiled Laws
A flood plain plan when the condominium lies within or abuts a flood plain area, showing all the following:
The location of all condominium buildings and improvements….
The contours over the entire project shown at 2-foot intervals.
Orthophotography is a geometrically corrected photograph created from either aerial or satellite imagery.
The most expensive part of producing an orhtophoto is generally the creation of the DEM.
The errors were larger on the edges of the orthophotograph and very small near the center of the image (nadir point).
From the results of experiments undertaken in this study, it is clear that these government datasets can be used to create orthophotos at a scale of 1:10,000 that meet acceptable industry standards such as those developed by ASPRS.
This study found that the SRTM data had slightly better accuracy than the NED data but it may not represent the terrain properly and may have larger errors in computing slope and aspect parameters. It is also important to note that the SRTM data is a DSM while NED data is a DEM measuring ground topography.
SRTM data is current which is an important advantage providing a proper model that can be used for many applications, even for updating the NED.
Strategies and methods for integrating data from different (and possibly diverse) sensors.
Process results maintain the highest accuracy and resolution existing within the original data
Interpolation is the procedure of predicting the value of an attribute at unsampled site from the measurements made at point locations within the same area or region.
Interpolate the value of geoid undulation between the grid values
Professor Georges Matheron (1930-2000) developed the formal foundation of Geostatistics, centered, in the beginning, on estimating changes in ore grade within a mine.
However, the principles have been applied to a variety of areas in geology and then to other scientific disciplines.
Geostatistical interpolation is known as kriging after D. G. Krige.
Some spatial surfaces cannot be modeled using deterministic methods that use smooth mathematical functions. Specifically if data are sparse, for example ground-water modeling, gravity data, soil mapping, water toxicity, air pollution, bathymetric data etc.
Kriging is a stochastic interpolation method in contrast with deterministic methods (TIN, Inverse distance, trend estimation).
It attempts to statistically obtain the optimal prediction i.e. to provide the Best Linear Unbiased Estimation (BLUE), specifically when data are sparse
The basic assumption is that the spatial variation can be expressed by the following summation:
z(s0) = m(s0) + x (s0) + e
m(s0) = deterministic function describing the ‘structural’ component of z
x(s0) = stochastic, spatially dependent residual from m(x)
e = Observational noise
Steps in the kriging interpolation process:
Explanatory data analysis; identify and eliminate outliers and trend ( compute m(s0) using Trend estimation )
Estimation of the variogram ( 2(h) )
Using the semi-variogram to perform kriging prediction
where is our interpolated point, z(si) are the sample points, and λi are kriging coefficients
4. MSPE calculation and error analysis (cross validation)
The Best Surveying Students!
The support for this research from the National Geospatial-Intelligence Agency under contract no. HM1582-04-1-2026 is greatly acknowledged.