GIS: Raster Analysis

Transcription

1 GIS: Raster Analysis Ts (K) High : 311 Low : Meters Albedo G / Rn High : 0.35 High : 0.50 Low : 0.03 Low :

2 Raster analysis: Outline Raster analysis applications Map algebra Local functions Neighborhood, zonal, distance, and global functions 2

3 Raster analysis: Applications Satellite image processing Map evapotranspiration, soil moisture, or plant biomass Determine land-cover change between two dates Determine the least cost path Remove noise within a raster 3

4 Map algebra Unary Binary 4

5 Local to global How do neighborhood functions differ when applied to raster versus vector data? 5

6 Raster analysis: Types of local functions Mathematical Boolean / logical Reclassification Multi-layer overlay 6

7 Local functions: Mathematical functions Most functions that can be applied to a single value can be applied to an entire raster Examples from METRIC Be mindful of Python syntax 1/2=0 3^2<>9 7

8 Local functions: Logical operations 0 = False; 1 = True Any non-zero value is true also Null assigned when any input is null NOT reverses True and False values XOR (exclusive OR) assigns TRUE when one input or the other is true, but not both 8

9 Local functions: Logical operators 9

10 Local functions: Comparison operators 10

11 Logical operators and categorical data? 11

12 Methods of reclassifying rasters 12

13 CONditional statements with rasters Decision-making capabilities Con(Layer1>Laye r2, math.pi*layer1, Layer3**.5) Python is case sensitive 13

14 Nested raster algebra operations Balance complexity with simplicity Deferred evaluation can make complex expressions execute faster than a string of simple expressions 14

15 Write a map algebra expression for E NB Allen, R.G., Tasumi, M., Morse, A., Trezza, R., Wright, J.L., Bastiaanssen, W., Kramber, W., Lorite, I.,Robison, C.W., Satellite-based energy balance for mapping evapotranspiration with internalized calibration (METRIC) - applications. Journal of Irrigation and Drainage Engineering-Asce 133(4),

16 KDo hor : Diffuse radiation index Given a KBo hor raster, write a map algebra expression to calculate KDo hor Allen, R.G., Trezza, R.,Tasumi, M., Analytical integrated functions for daily solar radiation on slopes. Agricultural and Forest Meteorology 139(1-2),

17 Raster overlay identifies each unique combination Restricted to nominal data Continuous data would cause too many unique combinations Convert continuous data to categories (H,M,L) 17

18 Overlay through raster addition 18

19 Use raster addition with unique combinations! 19

20 True raster clip operation Looks quite similar to multiplication, assuming zeros are not present in source data 20

21 Raster clip using multiplication 21

22 Neighborhood operations use moving window Surrounding cells determine value of center cell Slope Aspect Spatial correlation 22

23 Neighborhood functions and rasters Raster calculations on a DEM can produce many different outputs Calculate number of land cover types in a neighborhood 23

24 Majority filter can remove noise 24

25 Kernel: a set of constants for each cell What output does this moving window function yield? 25

26 Kernels can be modified for edges and corners 26

27 Applications of neighborhood functions Edge detection for soil boundary mapping Pattern detection Roughness Determining which slope function is optimal for a particular application Ashraf, M.I., Zhao, Z., Bourque, C.P.A.,Meng, F.-R., GIS-evaluation of two slope-calculation methods regarding their suitability in slope analysis using high-precision lidar digital elevation models. Hydrological Processes 26(8),

28 Neighborhood functions: Edge detection Subtract values on one side of kernel from values on other side Large differences yield large values If differences exceed threshold then edge is present 28

29 Boundary mapping Kernels to detect horizontal and vertical boundaries 29

30 Neighborhood functions: High pass filters High pass filters accentuate differences between adjacent cells Identify spikes or pits Large values are spikes Large negative values are pits 30

31 High pass filter Accentuating extreme values 31

32 Effects of mean on spatial autocorrelation 32

33 Zonal functions summarize statistics 33

34 Cost surfaces The cost of traveling across a surface can be measured in distance, time, or monetary cost A cost surface contains the minimum cost to reach a cell from one or more source cells Cost may be a constant function of distance or vary based on a friction surface Friction is the cost per unit travel distance 34

35 Cost surface calculations (Fixed cost) 35

36 Friction surfaces for variable travel costs 36

37 Row column calculations of cost surfaces 37

38 Cost surfaces and route optimization Given a cost surface algorithms can be used to predict the least-cost path Barriers can be placed to represent areas that cannot be traversed such as water or cliffs 38

39 Primary sources 39