Raster Analysis. Spatial Analysis (Raster) II. Vector/Raster Conversion operations Applying Boolean logic in raster
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1 Spatial Analysis (Raster) II GEOG 300, Lecture 11 Dr. Anthony Jjumba 1 Raster Analysis Vector/Raster Conversion operations Applying Boolean logic in raster 2 1
2 Raster vs. Vector Source Raster Data are modeled with a grid of cells. Each cell is described with one value Source 3 Converting points to raster If points clustered together, they could be converted to one raster cell. Point has area equal to cell size 4 2
3 Converting points to raster Illustration of boundary rules for point feature to raster conversion Source: ESRI 5 Lines to raster Lines are seen as contiguous pixels 6 3
4 Lines to raster Illustration polyline to raster conversion 7 Source: ESRI Vector to raster conversion Similar to scanning specify cell size (pixels) and the attribute used - Vector GIS handles attributes more effectively 8 4
5 Vector to raster conversion 9 Vector to raster conversion Illustration of six polygons that fall within a single cell 10 5
6 Polygons to raster areas have similar adjacent pixels Attribute table shows the number of pixels in each value, these are 11 graphed in a histogram Raster Pixel Depth Capacity for precision Size
7 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 13 Local Operations Reclassification Create a new raster layer by applying changes to 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 14 7
8 Reclassification 15 Reclassification 16 8
9 Overlay Analysis 17 Overlay Analysis 18 9
10 Overlay Analysis 19 Overlay Analysis 20 10
11 Overlay Analysis 21 Local Neighborhood Operations Illustration of a moving window Also known as focal operations. The neighborhood is defined by a moving window. The assumption is that the value at the center is affected by values in the neighborhood
12 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 23 Neighborhood Operations Operation: Summation (including value of focal cell) Neighborhood size: 3 x 3 rectangle e.g. to establish available food supply for wildlife24 12
13 Neighborhood Operations Summation (including value of focal cell) Other common applications: Data simplification (smoothing) Terrain analysis (local relief / roughness) Site selection 25 Raster Operations 26 13
14 Map Algebra (Raster Math) = = = 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 28 14
15 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 29 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 30 15
16 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 31 Raster Model: Roads vs. Cover Roads bad, conifer cover good Input 1: Input 2: Output: Road Distance Conifer Cover Habitat Quality 32 16
17 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 33 Model 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 34 Model 17
18 Euclidean Distance Straight-line distance from point, line, or poly 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 36 Habitat 18
19 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) 37 Pesky Parameter Problems The sly and elusive lynx avoids roads
20 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 39 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, 40 20
21 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 41 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 42 21
22 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 43 Road 0 50 : : : : 100 River 0 50 : : : : 10 Swamp : : : 15 NSR : : : 100 Coding the Parameters Variable Weight 100 = preferred habitat 75 = good but not great 50 = acceptable 25 = avoided 0 = terrible Distance Range (m): Weight 44 22
23 % of Landscape Devoted to Corridor Under Different Constraints 45 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 46 23
24 Observed Beetle Kill to Date 47 Projected
25 Projected Projected
26 Projected Projected
27 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) 53 27
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