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4/15/2015© 2009 Raymond P. Jefferis III Lect 04 - 1 Geographic Information Processing Raster Data Models Data files Metadata Cell size Cell alignment Resampling.

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Presentation on theme: "4/15/2015© 2009 Raymond P. Jefferis III Lect 04 - 1 Geographic Information Processing Raster Data Models Data files Metadata Cell size Cell alignment Resampling."— Presentation transcript:

1 4/15/2015© 2009 Raymond P. Jefferis III Lect Geographic Information Processing Raster Data Models Data files Metadata Cell size Cell alignment Resampling Smoothing Malvern Quadrangle - USGS DEM Data

2 4/15/2015© 2009 Raymond P. Jefferis III Lect Raster Data Files Taken at regular intervals covering an area The data cells are typically rectangular The area is defined by its corner coordinates The ordering of the data, within the file, determines the spatial location of each cell with respect to the corners

3 4/15/2015© 2009 Raymond P. Jefferis III Lect USGS Digital Raster Graphics Digital Geospatial Metadata 7.5-minute DRGs (scanned from older maps) Projection: typically 1927 North American Datum Available Scales: 1:12,000 (approx. 3.75' quadrangles) 1:24,000 (approx. 7.5' quadrangles - most common) 1:63,360 (approx. 15' quadrangles - abandoned) 1:100,000 (30' x 60' quadrangles) 1:250,000 (1˚ x 2˚ or 3 ˚ quadrangles) Data supplied in TIFF format

4 4/15/2015© 2009 Raymond P. Jefferis III Lect DRG Map Scale Comparison USGS Scale 1:24,000Scale 1:100,000

5 4/15/2015© 2009 Raymond P. Jefferis III Lect Digital Elevation Model (DEM) Data Arrays of regularly spaced elevations on south-to-north profiles, ordered west-to-east 7.5', 30', or 1˚ sets, skewed from longitudes ASCII or binary elevation values Universal Transverse Mercator (UTM) or geographic coordinate referenced Seamless version is National Elevation Data (NED)

6 4/15/2015© 2009 Raymond P. Jefferis III Lect DEM Availability Replaced by: – National Elevation Dataset (NED) and, – Spatial Data Transfer Standard (SDTS) data Download sites:

7 4/15/2015© 2009 Raymond P. Jefferis III Lect National Elevation Dataset (NED) Seamless raster dataset Available from USGS Elevations in meters Resolution: 1arc-sec (approx. 30-meters) Datum: North American Datum 1983 Metadata:

8 4/15/2015© 2009 Raymond P. Jefferis III Lect DEM Scan Format Standards for Digital Elevation models, Part 1, USGS

9 4/15/2015© 2009 Raymond P. Jefferis III Lect DEM Standards Download for use with your datasets. Download site:

10 4/15/2015© 2009 Raymond P. Jefferis III Lect Reading DEM Files in Mathematica ® ySR = Import[ "~/Desktop/DEMdata/PA/Malvern/ dem.sdts.tgz", {"SDTS", "SpatialRange"}] yER = Import[ "~/Desktop/DEMdata/PA/Malvern/ dem.sdts.tgz", {"SDTS", "ElevationRange"}] Spatial range result [meters]: {{446655, }, { , }} Elevation range result [feet]: {0, 720}

11 4/15/2015© 2009 Raymond P. Jefferis III Lect Extracting Data Parameters dims = Dimensions[yDat]; (* get metadata *) nbase = dims[[1]];(* Skip *) nrows = dims[[2]];(*number of data rows -> 465 *) ncols = dims[[3]]; (* number of data columns -> 358 *) minval = yER[[1]]; (* minimum altitude, feet *) maxval = yER[[2]]; (* maximmum altitude, feet *)

12 4/15/2015© 2009 Raymond P. Jefferis III Lect Reading DEM File in Mathematica ® Import data: yDat = Import[ "~/Desktop/DEMdata/PA/Malvern/ dem.sdts.tgz", {"SDTS", "Data"}]; Look at the elevation data of one row: y = yDat[[1, 465]] The result is: {0, 0, 0, 418, 408, 402, 394, 387, 375, 372, 386, 394, 393, 386, 378, 375, ¥ 372, 373, 373, 372, 371, 369, 361, 354, 348, 350, 350, 350, 350, 353, 354, ¥ 357, 358, 361, 364, 366, 368, 369, 379, 397, 407, 397, 388, 376, 367, 359, ¥ 352, 345, 340, 334, 329, 323, 316, 310, 306, 304, 304, 304, 305, 306, 308, ¥ 310, 312, 315, 320, 326, 330, 334, 337, 338, 338, 337, 334, 327, 322, 316, ¥ 304, 298, 292, 288, 286, 285, 285, 286, 290, 294, 302, 313, 321, 334, 350, ¥ 381, 400, 442, 486, 497, 483, 461, 449, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ¥ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ¥ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}

13 4/15/2015© 2009 Raymond P. Jefferis III Lect Viewing DEM File in Mathematica ® yGrf = Import[ "~/Desktop/DEMdata/PA/Malvern/ dem.sdts.tgz", "SDTS"]; Notes: Raster skew, leading to missing data, at edges of quadrangle Older file data Image slightly blurred

14 4/15/2015© 2009 Raymond P. Jefferis III Lect Plotting Data in Mathematica ® elev = Table[yDat[[1, r, c]], {r, 1, nrows, 1}, {c, 1, ncols, 1}]; ReliefPlot[elev, ColorFunction -> "GreenBrownTerrain"] Notes: First step makes table of sampled data, which can be used for further processing Second step plots it Image is crisp Pixels approx. 30 x 30 meters

15 4/15/2015© 2009 Raymond P. Jefferis III Lect Calculation, from Lecture #2 At 40˚ North latitude, 76˚ West longitude, a square of 7.5´ will have the planar dimensions: North_Distance = km meter cells East_Distance = km meter cells

16 4/15/2015© 2009 Raymond P. Jefferis III Lect Calculation, from File Data At 40˚ North latitude, ˚ West longitude, a square of 7.5´ will have the planar dimensions: North_Distance = km meter cells East_Distance = km meter cells Note: There is one more row and column in the file, so that the data go all the way to the edges.

17 4/15/2015© 2009 Raymond P. Jefferis III Lect Metadata Data about the data Gives resolution, units, datum, etc. Needed for interpreting data Data storage format given in specifications located separately

18 4/15/2015© 2009 Raymond P. Jefferis III Lect Selected Metadata ITEM_TYPE: SDTS DEM - 7.5X7.5 GRID CELL_NAME: Malvern X_RESOLUTION: 30 Y_RESOLUTION: 30 XY_UNITS: Meter Z_RESOLUTION: Z_UNITS: Foot HORIZONTAL_DATUM: North American Datum of 1927 PROJECTION: Transverse Mercator MIN_ELEVATION: 105 MAX_ELEVATION: 720 NORTH_LATITUDE: SOUTH_LATITUDE: WEST_LONGITUDE: EAST_LONGITUDE:

19 4/15/2015© 2009 Raymond P. Jefferis III Lect Calibration of Data Locate known landmark or benchmark feature coordinates Proportion pixel count to find data Draw mark at location on figure Replot data with modified feature Check with USGS topographic map

20 4/15/2015© 2009 Raymond P. Jefferis III Lect Example Antenna known to be on top of ridge: (* Bacton Hills Antenna Location *) antlat = ;[North] antlon = ;[West] Define quadrangle corners: (* Malvern Quadrangle Corners*) nelat = ; nelon = ; swlat = ; swlon = ;

21 4/15/2015© 2009 Raymond P. Jefferis III Lect Example (continued) Locate {x,y} data pixel of feature: (* Proportion Pixels from SW Corner*) latpt = Round[nrows*((antlat - swlat))/(nelat - swlat)]; lonpt = Round[ncols*((swlon - antlon))/(swlon - nelon)];

22 4/15/2015© 2009 Raymond P. Jefferis III Lect Example (continued) Draw crosshairs (set height to sea level!) (* Draw Crosshairs *) yDat[[1, latpt, lonpt]] = 0; yDat[[1, latpt + 1, lonpt]] = 0; yDat[[1, latpt + 2, lonpt]] = 0; yDat[[1, latpt - 1, lonpt]] = 0; yDat[[1, latpt - 2, lonpt]] = 0; yDat[[1, latpt, lonpt + 1]] = 0; yDat[[1, latpt, lonpt + 2]] = 0; yDat[[1, latpt, lonpt - 1]] = 0; yDat[[1, latpt, lonpt - 2]] = 0;

23 4/15/2015© 2009 Raymond P. Jefferis III Lect Example (plotted result) Crosshairs on ridgeline Location correct Plot oriented correctly Find corresponding Mathematica ® notebook in Models file as: MalvernBactonTest ==>

24 4/15/2015© 2009 Raymond P. Jefferis III Lect DTED Data File Format (1) User Header Label (UHL: 80 bytes) 1 (2) Data Set Identification Record (DSI: 648 bytes)81 (3) Accuracy Record (ACC: 2700 bytes)*729 (4) Data Records (3601 records at , 10642, 17856,etc. bytes/record)** ** The number of records is a function of the latitude. A count of 3601 is for cells between latitudes S50 and N50 degrees. Missing elevations are filled with 1 bits. Elevations are two-byte integers, high order first, and negatives are signed magnitude.

25 4/15/2015© 2009 Raymond P. Jefferis III Lect Reading DTED File See MalvernDTED notebook in Models directory

26 4/15/2015© 2009 Raymond P. Jefferis III Lect Reading DTED File Header (* Define and read in data for DTED data set *) Array[d, 2000, 2000]; s = OpenRead["~/Desktop/DTEDdata/w076n40.dt2"]; (*Read Header information *) uhl = ReadList[s, Character, 80]; dsi = ReadList[s, Character, 648]; acc = ReadList[s, Character, 2700];

27 4/15/2015© 2009 Raymond P. Jefferis III Lect Reading DTED File (* Read the data and convert to signed integer format *) (* Offset to Malvern Quadrangle *) mm = 1350; ll = 450; (* Width in rows *) For[j = mm, j < mm + ll, j++, { (* Read record header *) SetStreamPosition[s, j*7214]; bh = Read[s, Byte]; bl = Read[s, Byte]; c1 = 256*Read[s, Byte] + Read[s, Byte]; c2 = 256*Read[s, Byte] + Read[s, Byte]; c3 = 256*Read[s, Byte] + Read[s, Byte]; rr = 0;(* Read data column *) For[i = 0, i < ll + 1, i++,{ d[i, j - mm] = 256*Read[s, Byte] + Read[s, Byte]; }] }] Close[s];

28 4/15/2015© 2009 Raymond P. Jefferis III Lect Plotted Result Notes: (Malvern Quadrangle) High resolution [10 meters] Processing to Level 2 Data to edges One-column overlap at edges (451 columns)

29 4/15/2015© 2009 Raymond P. Jefferis III Lect Calibration Mark a known feature on plotted result and compare with its known location –Mountaintop –Stream intersection –Lake –Quarry

30 4/15/2015© 2009 Raymond P. Jefferis III Lect Calibration Notes: Crosshairs at Bacton Hill antenna site. Location correct

31 4/15/2015© 2009 Raymond P. Jefferis III Lect SRTM Files [.HGT ] Heights are signed two byte integers. The bytes are in Motorola "big-endian" order with the most significant byte first. Heights are in meters referenced to the WGS84/EGM96 geoid. Data voids are assigned the value SRTM1 files contain 3601 lines of 3601 samples each, with edge overlap. IMPORTANT! - NW-to-SE order

32 4/15/2015© 2009 Raymond P. Jefferis III Lect Reading SRTM1 Files See hgt2Test notebook in Models file Note: Data are in binary bytes, stored as rows (not columns), read from NW to SE (software will reverse).

33 4/15/2015© 2009 Raymond P. Jefferis III Lect Reading SRTM1 Files [.HGT] Array[d, 3601, 3601]; s = OpenRead["~/Desktop/N40W076.hgt", BinaryFormat -> True]; nn = 1350; nrows = 450; ncols = 450; For[j = 0, j < nrows + 1, j++, SetStreamPosition[s, nn* *( j)]; For[i = 0, i < ncols + 1, i++, d[i, j] = 256*BinaryRead[s, "UnsignedInteger8"] + BinaryRead[s, "UnsignedInteger8"] ]; ]; (* Close data file *) Close[s];

34 4/15/2015© 2009 Raymond P. Jefferis III Lect Results Notes: High resolution image Processed to remove voids No gaps at edges

35 4/15/2015© 2009 Raymond P. Jefferis III Lect Resampling Result is new image derived from original Each pixel is weighted sum of surrounding pixels Principal methods –Straight sampling (to reduce raster points) –Bilinear interpolation (for off-grid points) –Cubic convolution (for regular raster points)

36 4/15/2015© 2009 Raymond P. Jefferis III Lect Simple Resampling Pick every n th pixel No pixel averaging Result is fewer pixels but no noise reduction Images look “jaggy”

37 4/15/2015© 2009 Raymond P. Jefferis III Lect Simple Resampling [5:1] sampledelev = Table[d[r + rowmin, c + colmin], {r, 0, nrows, 5}, {c, 0, ncols, 5}]; ReliefPlot[sampledelev, AspectRatio -> / , ColorFunction -> "GreenBrownTerrain"] Note: Pixel aspect ratio is defined from calculation results of Slides 15 and 16.

38 4/15/2015© 2009 Raymond P. Jefferis III Lect Resampled 2:1 and 5:1 Quadrangles

39 4/15/2015© 2009 Raymond P. Jefferis III Lect Re-sampling with Smoothing Pick every n th pixel Form new value from weighted sum of this pixel and its surrounding ones. Result: fewer pixels, some noise reduction Images look “jaggy”

40 4/15/2015© 2009 Raymond P. Jefferis III Lect Resulting Resampled Data Fewer pixels per square ground area More area can be covered Note: SRTM3 data could be used instead –30 x 30 meter (approx.) pixels –Each pixel averages nine (9) SRTM1 pixels –Averaging improves statistical accuracy

41 4/15/2015© 2009 Raymond P. Jefferis III Lect Bilinear Interpolation Pixel d 33 can be off-grid, others are nearby points for extrapolation Pixel d 33 is to be distance-weighted sum of pixels d 22, d 24, d 42, and d 44 x-distance is (x 33 /x 32 )*(x 34 -x 32 ) y-distance is (y 33 /y 43 )*(y 23 -y 43 ) Portion of data to be resampled to give value of data at new point, d 33

42 4/15/2015© 2009 Raymond P. Jefferis III Lect Convolution Value of raster point determined from weighted sum of others Simple averaging includes the 8 closest pixels, weighted by distance from the center pixel Cubic convolution uses the 16 closest pixels Convolution kernel needed Resulting values should be normalized by the sum of weights to retain proper scale

43 4/15/2015© 2009 Raymond P. Jefferis III Lect Averaging Convolution Kernel Note: Cells are weighted by distance from the center cell, and the array of weights is then normalized by the sum of weights.

44 4/15/2015© 2009 Raymond P. Jefferis III Lect Results Raw data represent 10 x 10 meter areas Averaged data represent 30 x 30 meter areas Raw data images are sharp; averaged data appear blurry. Raw data contours are jaggy; contours of averaged data are smoother. Images follow:

45 4/15/2015© 2009 Raymond P. Jefferis III Lect Raw and Averaged Data Images Raw Data - MalvernAveraged Data

46 4/15/2015© 2009 Raymond P. Jefferis III Lect Raw and Averaged Contours Raw Data - MalvernAveraged Data

47 4/15/2015© 2009 Raymond P. Jefferis III Lect Contours - Summary Smoothing important for contour maps Convolution is effective for processing

48 Questions? 4/15/2015© 2009 Raymond P. Jefferis III Lect


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