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 attributes linked to feature ID explicit location every point has coordinates 2

3 Fields in GIS continuous f(x, y) so how represent geometry? attributes? location? 3

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

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

6 Characteristics of Rasters Cell size defines level of spatial detail all variation within cells is lost for given area: cell size #cells data volume Cell value characteristic of spatial phenomenon at cell location ArcGIS: integer or floating-point assignment scheme average over cell total within cell commonest value in cell value at cell s central point 6

7 Characteristics of Rasters (cont d) Bands (channels) single multiple bands e.g. red,green,blue color image Implicit geometry location of any cell can be calculated from grid location cell size Spatial reference map implicit external coordinates e.g. in a map projection 7

8 Raster Datasets Name Structure cells aka pixels columns rows Value attribute key zone NoData ignored or propagated 8

9 Raster Attributes Cell value attribute table row count (always) anything else 9

10 Raster Coordinates left left 10

11 Raster Examples USGS 1-meter DOQ digital orthophoto quadrangle USGS 1:24000 DRG digital raster graphic scanned from 7.5 topo 11

12 Vector Raster Conversion Rasterize = cells that intersect feature Vectorize = outline contiguous region 12

13 Note: Distance vs Buffering Buffer: discrete Distance: continuous 13

14 Feature Representation in Rasters: Sub-pixel Features Coarsened 14

15 Feature Representation in Rasters: Large Features Blurred Tree represented as varying values of treeness, instead of as a crisp feature 15

16 Raster Operations in order of increasing #input cells contributing to 1 output cell Local Focal aka neighborhood Zonal Global 16

17 Local Operations out(xi, yj) = f(in(xi, yj)) Neighbors don't influence Examples reclassify select min/max Think of as: Solve for all unique cell values; then reclassify 17

18 Local Operation Example: 1 Input Reclassify (change values using lookup table) IN OUT

19 Local Operation Example: d = mean(a, b, c) shaded = NoData NB: NoData in any input NoData in output Multiple Inputs 19

20 Extent restrict processing to rectangular subset explicit: (xmin, ymin, xmax, ymax) 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) Operations out(xi, yj) = f(in(xk, ym) 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 Operation Example: Mean b = 3x3 mean (a) e.g = ( ) / 9 26

27 Zones Zone = all cells with same value 27

28 Regions Region = contiguous cells with same value 28

29 Zonal Operations out(xi, yj) = f(in(xk, ym) k,m zone(xk, ym) = zone(xi, yj)) like focal, but uses zone for neighborhood replace cell value with some property of its neighbors in zone it overlaps 29

30 Zonal Operations (cont'd) ArcGIS supports majority, median, minority maximum, range, minimum sum, mean, standard deviation variety (# distinct values) Examples forest zone species variety elevation zone mean snow depth 30

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

32 Global Operations out(xi, yj) = f(in(xk, ym) k,m) each output cell depends on all input cells can be computationally intensive Examples distance variogram 32

33 Global Operation Example: Re: nearest source cell: value distance direction Euclidean Allocation 33

34 Figure Credits Introduction to Geographic Information Systems, 4 th ed. ISBN ArcMap Help Modeling Our World. ISBN Geographic Information Systems and Science, 2nd ed. ISBN ArcGIS 9: Using ArcGIS Spatial Analyst 34