Loading in 2 Seconds...
Loading in 2 Seconds...
Mark J. Schwab 1 , Michael Stanley 1 , Siwei Xu 1 , Jon Pals 1 , Mary J. Brodzik 2 , Cathy Fowler 2. 1 Raytheon 2 NSIDC. STEP 1 GRID CONSTRUCTION a) Find Maximum and Minimum Latitude and Longitude values for all data points in the data file.
Mark J. Schwab1, Michael Stanley1, SiweiXu1, Jon Pals1, Mary J. Brodzik2, Cathy Fowler2
STEP 1 GRID CONSTRUCTION
a) Find Maximum and Minimum Latitude and Longitude values for all data points in the data file.
b) Starting with a configurable minimum cell size calculate how many grid cells would exist in the grid. Double cell dimensions until the number of grid cells falls below a configurable maximum threshold.
c) Create the final grid as a two dimensional list of grid cells.
STEP 2 MARK GRID CELLS WHICH CONTAIN DATA POINTS ( FIGURE 1)
a) Starting with the first data point position in the data file, determine which cell it is in and mark it as containing data.
b) For every point, if it is not in the last marked grid cell, hash to the cell in the grid that it might be inside.
c) If the data point is not in the hashed cell, then follow the line from the center of the previously marked cell to that point, and move along that line to each cell in between.
d) When a cell is found that contains the point, mark it as having data, and it takes the place of last marked cell.
e) Repeat until all data points from the file have been tested.
Figure 3: Using the same data swath this shows the points chosen in Step 6 to use as vertices for the polygon, shown in blue, as the algorithm walks around the outer edge of the selected cells.
Figure 6: Close up of reduced polygon points. Compare with number of points in Figure 5.
Figure 1: This shows a subset of the location information for data collection sites for the LVIS instrument. The instrument sweeps side to side as the airplane flies creating a “swath” of data. Every cell that contains data from the original grid created in step 1 gets marked.
Polygons using a relatively small number of vertices can be created to approximate geo-location of data collection sites from a variety of instrument types without any change to the algorithm. Some results are shown in Figures 7(a-d).
Due to the volume of data points collected from instruments on airborne/aircraft data collection missions and the nature of the flight paths themselves it is difficult to produce reliable spatial metadata for the data files collected during these flights. We outline one approach that we found worked well for Operation IceBridgedata sets. We use the algorithm to create Geo-Polygons with a sufficiently small number of vertices for efficient spatial searching in the EOSDIS Core System (ECS). These spatial metadata will allow users to efficiently and effectively search IceBridgeaircraft data through ECHO/Reverb and any other tool created to interface with these metadata holdings, including the IceBridgedata portal which is currently under development.
Figure 4B: Zoom of Exclusion zones. All of the cells inside the Exterior Polygon get marked and are shown in blue.
Figure 4A: Sample of data path taken by an aircraft which includes enclosed loops that we want excluded from spatial metadata. The grey area is used in subsequent images for illustrative purposes.
Figure 7B: Data swath from sweeping laser measuring elevation with hundreds of thousands of data points can be spatially represented with a small fraction of the original number of points.
Figure 7A: Data path from airborne radar instrument. Exterior polygon and multiple exclusion zones are shown.
Figure 2: This figure shows the same subset of LVIS data. The green shows the buffer cells added to the outer edge of all of the data cells and the cells along the line used to bridge gaps (Step 3) in the data.
Figure 7D: Data path from airborne radar instrument. The algorithm follows the contours tightly and follows the spatial extent much better than a convex hull algorithm .
Figure 7C: Data path from airborne radar instrument. Notice the single data point captured by itself and the small number of points used to represent the spatial extent.
Corresponding author: Mark SchwabEmail: email@example.com Phone: +1.303.735.3061
AGU Fall Meeting IN51B-1586, 9 December 2011
Figure 5: Resultant Polygon and Exclusion zones. The exclusion zones have been made into distinct polygons.