Raster Data. James Frew ESM 263 Winter

Transcription

1 Raster Data 1

2 Vector Data Review discrete objects geometry = points by themselves connected lines closed polygons agributes linked to feature ID explicit localon every point has coordinates 2

3 Fields in GIS conlnuous f(x, y) so how represent geometry? agributes? localon? 3

4 ApproximaLng Fields Point set regular irregular Grid Polygons TIN Contours 4

5 Sampled Fields: Rasters Divide (part of) the world into square cells (aka pixels) Register the corners to the Earth Represent fields by assigning agribute values to cells Represent discrete objects as colleclons of one or more cells More commonly used to represent fields than discrete objects 5

6 Raster Data Model Cell size defines level of spalal detail all varialon within cells is lost #cells data volume Cell value field value w/in cell average? total? modal? central point? Implicit geometry grid cell (pixel) coordinates 6

7 Raster Coordinates convert raster (row, column) to map (x, y) using affine transform transform parameters may be saved in world file 7

8 CharacterisLcs of Rasters (cont d) Bands (channels) single mullple e.g., RGB color 8

9 Raster AGributes Cell value agribute table row count (always) anything else 9

10 Raster Examples aerial image scanned topo map 10

11 Vector Raster Conversion Rasterize = cells that intersect feature Vectorize = outline conlguous region 11

12 Note: Distance vs Buffering Buffer: discrete Distance: conlnuous 12

13 Feature RepresentaLon in Rasters: Sub-pixel Features Coarsened 13

14 Feature RepresentaLon in Rasters: Large Features Blurred Tree represented as varying values of treeness, instead of as a crisp feature 14

15 Raster OperaLons in order of increasing #input cells contribulng to 1 output cell Local Focal aka neighborhood Zonal Global 15

16 Local OperaLons out(x i, y j ) = f(in(x i, y j )) Neighbors don't influence Examples reclassify select min/max Think of as: Solve for all unique cell values; then reclassify 16

17 Local OperaLon Example: 1 Input Reclassify (change values using lookup table) IN OUT

18 Local OperaLon Example: MulLple Inputs d = mean(a, b, c) shaded = NoData NB: NoData in any input NoData in output 18

19 Local OperaLon Example: Combine d = code for unique values of (a,b) NB: #d product (#unique a x #unique b) i.e. may not fit in same representalon as a or b c = d(a,b) 19

20 Extent restrict processing to rectangular subset explicit: (x min, y min, x max, y max ) implicit: dataset bounding box 20

21 works with features, too 21

22 Mask Restrict processing to coincident defined cells i.e. anything except NoData 22

23 Extract extract by polygon extract by mask 23

24 Focal (Neighborhood) OperaLons out(x i, y j ) = f(in(x k, y m ) k,m near i,j) single cell and its neighbors Examples smooth sharpen noise suppresion Think of as weighted sum or sort and pick 24

25 Neighborhood Types Four common neighborhood types rectangle circle annulus wedge (x = focal cell) 25

26 Focal OperaLon Example: Mean b = 3x3 mean (a) e.g = ( ) / 9 26

27 Zones Zone = all cells with same value 27

28 Regions Region = con:guous cells with same value 28

29 Zonal OperaLons out(x i, y j ) = f(in(x k, y m ) k,m zone(x k, y m ) = zone(x i, y j )) like focal, but uses zone for neighborhood replace cell value with some property of its neighbors in zone it overlaps 29

30 Zonal OperaLons (cont'd) ArcGIS supports majority, median, minority maximum, range, minimum sum, mean, standard devialon variety (# dislnct values) Examples forest zone species variety elevalon zone mean snow depth 30

31 Zonal OperaLon Example: Zonal Mean c = zonal mean (a, zone b) e.g = mean(zone 1: {1, 1, 2, 2, 4, 3}) 31

32 Implicit Zone: Aggregate b = lower-resolulon(a) low-res cell = mean(overlapped high-res cells) 32

33 Global OperaLons out(x i, y j ) = f(in(x k, y m ) k,m) each output cell depends on all input cells can be computalonally intensive Examples distance variogram 33

34 Global OperaLon Example: Euclidean AllocaLon Re: nearest source cell: value distance direclon 34

35 Figure Credits IntroducLon to Geographic InformaLon Systems, 4 th ed. ISBN ArcMap Help Modeling Our World. ISBN Geographic InformaLon Systems and Science, 2nd ed. ISBN ArcGIS 9: Using ArcGIS SpaLal Analyst 35