Spatial Analysis (Raster) II

Transcription

1 Spatial Analysis (Raster) II GEOG 300, Lecture 10 Dr. Anthony Jjumba 1 Announcement Wildlife Society Student Chapter On Tuesday, 17 Oct and Wednesday, 18 Oct bake sale to fundraise money for the reef tank in building 8. some fish were lost recently, and the proceeds of the bake sale will go to adding more fish to the tank. Please attend and support. 2 1

2 Review GIS: Definition Description Applications/Functions of GIS Operations of a GIS The nature of Geographic Phenomena Spatial data Scale, Precision and Accuracy Map design 3 Review Coordinate Systems Geographic Coordinate Systems Projected Coordinate Systems Ellipsoid Mean sea level Geoid Datum 4 2

3 Review Map Projections Major projection classes/properties Tissot Indicatrix Common Projections UTM What is the appropriate map projection for Canada as whole? 5 Review Sampling methods Measurement Scales Vector Analysis Topological Analysis Non-topological Analysis Network Analysis Point Pattern Analysis Interpolation Density Estimation 6 3

4 Raster Analysis Basics Applying Boolean logic in raster Comparable vector operations 7 Raster vs. Vector Source Raster Data are modeled with a grid of cells. Each cell is described with one value Source 8 4

5 Converting points to raster If points clustered together, they could be converted to one raster cell. Point has area equal to cell size 9 Converting points to raster Illustration of boundary rules for point feature to raster conversion Source: ESRI 10 5

6 Lines to raster Lines are seen as contiguous pixels 11 Lines to raster Illustration polyline to raster conversion 12 Source: ESRI 6

7 Vector to raster conversion Similar to scanning specify cell size (pixels) and the attribute use - Vector GIS handles attributes more effectively 13 Vector to raster conversion

8 Vector to raster conversion Illustration of six polygons that fall within a single cell 15 Polygons to raster areas have similar adjacent pixels Attribute table shows the number of pixels in each value, these are 16 graphed in a histogram 8

9 Raster Operations Raster operations: grouped according to the way raster cells are used in the analysis Local Operations: value of the cell in the output layer is a function of the cell at the same location in the input layer Neighborhood Operations: value of the cell in the output layer is a function of the cells neighboring the cell at the same location in the input layer Extended Neighborhood Operations: value of the cell in the output layer is a function of the cells neighboring and beyond the immediate neighborhood of the cell at the same location in the input layer Regional Operations: the output layer us generated by identifying cells that intersect with or fall within each region on the input layer 17 Raster Operations 18 9

10 Local Operations Reclassification Create a new raster layer by changing the attribute values of the cells in the input layer Logical or arithmetic operations Binary masking; Classification reduction; Classification Ranking; Changing Measurement Scales Overlay Analysis Logical or arithmetic operations AND, OR, XOR; addition, subtraction, multiplication, division, assignment Two or more input layers 19 Reclassification 20 10

11 Reclassification 21 Overlay Analysis 22 11

12 Overlay Analysis 23 Overlay Analysis 24 12

13 Overlay Analysis 25 Overlay Analysis 26 13

14 Overlay Analysis 27 Local Neighborhood Operations Illustration of a moving window Also known as focal operations. The neighborhood is defined by a moving Window. Assumption is that the value of Is affected by values in the neighborhood

15 Local Neighborhood Operations Averaging method Computes average value of the cells over the window and uses as the value of the aggregated cell Central cell method Assumes the value of cell at the center of the window to be the value of the aggregated cell Median cell method Computes the median value of all the cells over the window and uses at as the vale of the aggregated cell 29 Neighborhood Operations Operation: Summation (including value of focal cell) Neighborhood size: 3 x 3 rectangle e.g. to establish available food supply for wildlife30 15

16 Neighborhood Operations Summation (including value of focal cell) Other common applications: Data simplification (smoothing) Terrain analysis (local relief / roughness) Site selection 31 Raster Pixel Depth Capacity for precision Size

17 Boolean Logic in Raster Analysis Create an expression reducible to true/false Binary examples Landscape examples 33 Raster Math = = =

18 Boolean Logic: AND 0 * 1 = 0 (false) 1 * 0 = 0 (false) 1 * 1 = 1 (true) Looking for a 1 Input 1: Input 2: Output: Good Reviews Open Tables Potential Dining 35 Boolean Logic: OR = 0 (false) = 1 (true) = 1 (true) = 2 (true) Looking for => 1 Input 1: Input 2: Output: Good Reviews Open Tables Potential Dining 36 18

19 Boolean Logic: XOR = 0 (false) = 1 (true) = 1 (true) = 2 (false) x Looking for = 1 Input 1: Input 2: Output: Good Reviews Open Tables Potential Dining 37 Boolean Logic: NOT 0-1 = -1 (false) 1-0 = 1 (true) 1-1 = 0 (false) Looking for = 1 Input 1: Input 2: Output: Good Reviews Open Tables Potential Dining 38 19

20 Raster Model: Roads vs. Cover Roads bad, conifer cover good Input 1: Input 2: Output: Road Distance Conifer Cover Habitat Quality 39 What is a Habitat Model? Basic needs: food, water, shelter Each variable ranked Sum of the inputs = overall quality Now pick it apart, what s the problem? Food Water Shelter Habitat 40 Model 20

21 One Step Up: >0 Requirement Basic needs: food, water, shelter If any of the inputs are zero, output = 0 (as no shelter = dead) Food Water Shelter Habitat 41 Model Euclidean Distance Straight-line distance from point, line, or poly

22 Using Euclidean Distance Distance from roads vs. Distance to Rivers Input 1: Input 2: Output: River Distance (under 80 good) Road Distance (under 80 bad) Acceptable 43 Habitat Clip vs Euclidian Distance Clip: Is it, or is it not, within distance X Yes or no Will chop a feature in half Raster: Average distance from pixel to X Distance at whatever precision specified But never absolutely precise (vs. point) 44 22

23 Pesky Parameter Problems The sly and elusive lynx avoids roads. 45 Expert-Based Habitat Modeling 1. Ask a trapper where (s)he sees bears in spring 2. Identify key characteristics Distance to roads Distance to eskers Distance to old forest Distance to swamp 3. Extrapolate to the landscape 46 23

24 Work-Through Lacking experts, any parameters will do Twas brillig, and the slithy toves Did gyre and gimble in the wabe; All mimsy were the borogoves, And the mome raths outgrabe. Beware the Jabberwock, my son The jaws that bite, the claws that catch! Beware the Jubjub bird, and shun The frumious Bandersnatch! He took his vorpal sword in hand; Long time the manxome foe he sought So rested he by the Tumtum tree, And stood awhile in thought. And, as in uffish thought he stood, The Jabberwock, with eyes of flame, Came whiffling through the tulgey wood, And burbled as it came! One, two! One, two! And through and through The vorpal blade went snicker-snack! He left it dead, and with its head He went galumphing back. And hast thou slain the Jabberwock? Come to my arms, my beamish boy! O frabjous day! Callooh! Callay! He chortled in his joy. Twas brillig, and the slithy toves Did gyre and gimble in the wabe; All mimsy were the borogoves, 47 Objective Identify routes that the Jabberwocky is likely to use as it travels from Purden Provincial Park to Aleza Lake Ecological Reserve and prioritize them according to the % of the landscape that may be devoted to Jabberwocky conservation 48 24

25 Toolset: Corridor Design Assumption: animal movement follows path of least risk (food, water, cover) Food, water, cover differ by species By finding routes that provide food, water, cover, we can maintain a travel corridor between patches 49 Parameters During the summertime, when adventure-seeking knights (and graduate students) roam the countryside, the Jabberwocky tends to avoid travelled roads. Rivers and swamps are its preferred haunts, where Bandersnatches and Jubjub birds are present to keep watch for would-be heroes. Finally, the creature is easily scared off by its arch nemesis the feller-buncher, and does not return to a stand until the area has been successfully regenerated. Jabberwocky will prefer to be 100m or more from roads Less than 50m from a river Less than 100m from a swamp More than 500m from an not-successfully regenerated block But Jabberwocky will compromise as necessary 50 25

26 Road 0 50 : : : : 100 River 0 50 : : : : 10 Swamp : : : 15 Coding the Parameters Variable Weight 100 = preferred habitat 75 = good but not great 50 = acceptable 25 = avoided 0 = terrible NSR : : : 100 Distance Range (m): Weight 51 % of Landscape Devoted to Corridor Under Different Constraints 52 26

27 Projection Model: Pine Beetle Mountain Pine Beelte Projection Parameters: Pine in a suitable biogeoclimatic zone Stand age >60 Local beetle pressure (powered flight) Regional beetle pressure (wind transport) At or below most northerly observed latitude Observed two years running KEY POINT 53 Observed Beetle Kill to Date 54 27

28 Projected Projected

29 Projected Projected

30 Projected What s Missing? Mountain porcupine beetle? Topographical barriers Mountains tend to get in the way Fine-scale population data Marginal vs. optimal habitat Max. range defined by latitude alone Vs. effective latitude (incorporating elevation) 60 30

31 Raster Applications: Site C Dam is at elevation X; water finds its level 61 Summary Raster math works like normal math (sorry) Boolean logic is foundational Remember those Venn diagrams! Wildlife applications next week 62 31

32 Colors: Consider the Following Red/green colorblindness 8% of men, 0.5% of women Color maps on B+W printer 63 Lightness (Value) 64 32

33 Saturation 65 Hue 66 33

34 Printers vs. Photocopiers Colorbrewer2.org B+W photocopiers Older printers (where I got my trust issues) Newer printers do better (examples on hand) 67 Take-Home Lightness (Value) always works Safest bet if you can t control the printer Saturation should work If you re working in-house If you re contracting a print job Hue sometimes works Some hues stand out, some don t Mileage may vary 68 34

35 69 Zonal Statistics Least Cost Path Analysis 70 35

36 Lam, N "Spatial Interpolation Methods: A Review," The American Cartographer. 10(2): Network Analysis Spatial Stats Excel skills 72 36

37 Spatial decision support systems (SDSS) SDSS: A category of information systems composed of a database, GIS software, models, and a so-called knowledge engine which allow users to deal with specific locational problems. Knowledge engine supports the particular decision making process e.g. community planning 73 37