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Lecture 11

by: Sierra

Lecture 11 FNR 210


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Raster Analysis & GIS Models
Natural Resource Information Management
Ningning N. Kong
Class Notes
Natural Resources, information management, forestry
25 ?




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This 37 page Class Notes was uploaded by Sierra on Thursday March 31, 2016. The Class Notes belongs to FNR 210 at Purdue University taught by Ningning N. Kong in Spring 2016. Since its upload, it has received 10 views. For similar materials see Natural Resource Information Management in Agriculture and Forestry at Purdue University.

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Date Created: 03/31/16
 Z-factor ???? ∗???????????????????????????? ????????????????????% = *100 ???? 1  All the followings have been graded: ◦ Quiz (8 X 10) ◦ Lab Quiz (4 X 10) ◦ Project Part 1 (50) ◦ Lab 1 – 6 (6 X 30)  Total score so far: 350 2 Raster Analysis & GIS Models 3  Map algebra is a way to perform spatial analysis for raster dataset(s).  It is – cell by cell combination of raster data layers  Each number in a raster represents a value at a cell location  Simple operations can be applied to each number 4  Raster layers may be combined through operations – addition, subtraction and multiplication 5  In may algebra, normally, raster layers should have the same extent, and the same cell size.  See example of issue to the right  it is recommended that you project the raster directly before performing the analysis. 6  You need “Spatial Analyst” extension for Map Algebra. 7  In ArcGIS, Raster Calculator is used to implement various operations. 8  Layer1 “operator” Layer2 ◦ Layer 1 is a raster layer ◦ Operator (mathematical, Boolean or logical) ◦ Layer 2 is single number, or another raster layer  Example ◦ Layer 1 + 5 ◦ Layer 1 * Layer 2 ◦ Layer 1 > Layer 2 9  When study landcover change ID 3 & 4 have several meanings 10  Multiply Layer A by 10 11  Using Map Algebra to analyze landscape change: Land Cover Map - 2006 12  Using Map Algebra to analyze landscape change: Land Cover Map - 2006 Land Cover Map - 2001 Old valueNew value Value X 100 11 1100 11 Result 21 2100 21 1111 22 2200 22 2181 23 2300 +  2121 23 2131 24 2400 24 … 31 3100 31 … 41 4100 41 … 13  Clip (or selection of areas) 14 14  Analysis: Determine slope on lands classified as forest ◦ Clip or select only the Forest cells from a landuse raster. ◦ Reclass all forest cells to 1 and all others to 0 creating a new binary raster with 0 or 1. ◦ To obtain the slope cells associated with forest, multiply these rasters by the binary raster ◦ Results: Cells in the forest class will have a value and those not in forest will be 0. 15  The operations of cell-based analysis available in Spatial Analyst can be divided into five types: ◦ Local operation – work on single cell locations ◦ Neighborhood (or Focal) operation – work on cell locations within a neighborhood ◦ Zonal operation – work on cell locations within zones ◦ Global operation – work on all cells within the raster ◦ Others – for specific application 16  Scope: use only the data at one input data point (usually the same point in each layer(s)) is used to determine value at a corresponding output location) 17  What is the value of the highlighted cell A. 16 B. 20 C. 30 D. 32 E. 47 18  Scope: (data from both an input location plus nearby locations to determine the output value) 19  Moving Windows can be any size; often odd (3X3, 5X5) to provide a center cell  Output raster is the results of the computation involving the neighbor cells placed in the center cell 20  Simple functions  Result is usually associated with the cell at the center;  The 3 x3 neighborhood, nine cells, are used as input for the function 21  What is the value of the blue cell? A. 1 B. 2 C. 3 D. 5 E. 9 22  What is the value of the blue cell? A. 10 Neighborhood Majority B. 12 C. 14 D. 18 E. 50 23  Calculates for each input cell location a statistic of the values within a specified neighborhood around it. 24  Definition of a “zone”: ◦ Raster cell with same value ◦ Vector polygon with same attribute  Similar with focal operation except the “neighborhood” definition is replaced by “zone”  Individual zones can be of any shape or size and can be disconnected from each other 25  Zones are defined by another layer. Can be any shape (polygon)  Creates a table containing the statistics for each zone  For example, average slope within watershed zones or max population within U.S. counties, etc. 26  What is the value of zone A? Zonal Minimum A. 3 B. 6 C. 9 D. 12 E. 13 27  Expands specified zones of a raster by a specified number of cells. 28  Scope (values from the entire input layer to determine each output value) 29  Euclidean distance Euclidean distance global operations assign to each cell in the output raster dataset its distance from the closest source cell. Source: location of the objects of interest, such as wells, shopping malls, roads, and forest stands. Output: floating-point distance values. 30 Non-zero values are “true”, zero values are “false” N = null values Non-zero values are “true”, zero values are “false” N = null values  What is the value of the blue cell? A. 0 B. 1 C. N 33  The process of using combinations of commands to answer questions about spatial phenomena.  Is a set of interacting, ordered map operations that act on raw data, to simulate a spatial decision- making process. 34  Analyze an area for Prime Ruffed Grouse habitat:  3 criteria of habitats  Size of habitat – buffer distance ◦ vector data ◦ raster data 35  Step 1: State the problem  Step 2: Break the problem down ◦ Query out 3 types of habitat ◦ Buffer ◦ Common areas  Step 3: Explore input datasets  Step 4: Perform analysis  Step 5: Verify the model result  Step 6: Implement the result 36  Raster overlay – 3 kinds of habitats  Buffer?  Focal statistics  Which value in the result should be considered as potential habitat? 37


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