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Exam 3 Study Guide

by: Sierra

Exam 3 Study Guide FNR 210


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These notes cover what's going to be on the next exam
Natural Resource Information Management
Ningning N. Kong
Study Guide
Natural Resources, information management, forestry
50 ?




Popular in Natural Resource Information Management

Popular in Agriculture and Forestry

This 78 page Study Guide was uploaded by Sierra on Thursday March 31, 2016. The Study Guide belongs to FNR 210 at Purdue University taught by Ningning N. Kong in Spring 2016. Since its upload, it has received 24 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
 Overlay ◦ Union Keep the spatial coverage from all input layers ◦ Intersect Only keep the overlap area ◦ Clip Intersect Union Clip 1  Overlay ◦ Union Keep the attributes info from all input layers ◦ Intersect ◦ Clip Only keep the attributes info from the layer to be clipped 2  Buffer Fixed Distance Varied distance from an attribute field Simple buffer – overlap dissolved 3  Dissolve – aggregate features based on specific attributes. One row in attribute table Dissolve function doesn’t consider spatial adjacency: Result: multipart polygon (or feature) 4 True/False  In a buffer analysis, you can apply different buffer distances to the features in a same polygon shapefile. ◦ True  The distance can be fixed, or be read from the attribute table.  Overlay analysis is only applicable to polygon layers. ◦ False 5  Which of the following vector based GIS analysis keeps the common spatial area from both input layers, but only keep the attribute value from one input layer? A. Union B. Intersect C. Clip D. Overlay 6  You can use the same clipping tool to clip vector and raster layers. Clip Raster Clip Vector 7  Dissolve function aggregates features based on the same value combinations and spatial adjacency. Based on spatial attribute only! Include: A single attribute Attribute combination 8  Study area: Define your study area. If the boundary file doesn’t exist, create one and digitize the boundary. Lab 4, Lab 5, Lab Quiz 3.  Coordinate System: Define your layer coordinate system. Important for part 2 of the project. – Check with Teaching Assistants if you need help.  Some of you have worked on part 2. Still need a new submission of part 2. 9  Part 1: ◦ Data Layers:  Roads  Streams  Lakes and other water bodies  Ortho  DEM ◦ Optional:  Soils, rails, land user, other 10  Part 2: GIS analysis ◦ Select at least 3 analysis  Query (select by attribute or location) & Export Data  Buffer  Overlay (clip, or union, or intersect)  Create & edit data (create new vector layer, or georectification)  Terrain modeling (slope, aspect, hillshade, and viewshed)  Raster calculator  Reclassification  Spatial interpolation 11  Due Friday, 3:30 pm, April 22, 2016.  Report: submit to blackboard – for each analysis ◦ Spatial question, purpose of analysis ◦ Detailed analysis steps ◦ Maps & tables of your analysis results ◦ Conclusion ◦ Reference & Acknowledgement  Data on N drive: ProjectPart2.mxd Start Early! 12  Query  Field Statistics  Review lab in week 8. 13 Terrain Analysis 14  Raster consists of a matrix of cells (or pixels) organized into rows and columns where each cell contains a value representing information. 15  Each cell consists of the same width and height.  The dimension of the cells can be as large or as small as needed.  The size determines how coarse or fine the patterns of features in the raster will appear. 16  Raster datasets with continues values don’t have attribute table.  Raster with categorical values, or integer values can have associated attribute value. 17  DEM – Digital Elevation Model  T errain determines the natural availability and location of surface water, and hence soil moisture and drainage.  T errain defines watershed boundaries and hydrologic networks. 18  When you add your raster dataset into ArcMap, there are times that ArcMap asking if you want to generate pyramid.  Pyramids are reduced-resolution representations of your dataset and are used to improve performance.  Pyramids can speed up display of raster data by retrieving only the data at a specified resolution required for the display.  Pyramids are created by resampling the original data.  Reading your source tab in properties dialog box:  Adjust the symbology Most often used Variable Description Importance Height Elevation above base Temperature, vegetation, visibility Slope Rise relative to Water flow, erosion, travel cost, horizontal distance construction suitability, habitat suitability, geology Aspect Downhill direction of Temperature, vegetation, steepest slope moisture, insolation Contours Lines that connect Shows how values change across locations of equal a surface elevation Hillshade Shaded relief from DEM Visual effect, background overlay by considering the illumination source angle and shadows 22  In ArcMap, you will need extensions to work with these terrain variables. 23  Slope is defined as the change of elevation ( a ris) with a change in horizontal position ( a run).  Slope is often reported in degrees ( 0° is flat, 90° is ve)tical  Sometimes, slope is also reported in percent.  Horizontal and Vertical Measurements must be in the same units.  Don't use USGS DEM data that has horizontal units in decimal seconds with slope, hillshade, visibility, and curvature. Project it into a planar coordinate system before using any of the above functions.  Z-factor is the essential parameter in terrain variables calculation.  Definition: Number of ground x,y units in one surface z unit  Z-value: hight  Z-value * Z-factor -> same measurement unit as XY  Example: If your z units are feet and your x,y units are meters, you would use a z-factor of 0.3048 to convert your z units from feet to meters (1 foot = 0.3048 meter).  If XY units are decimal degrees, and z-unit are meters: Inclination of slope  As % rise  or in degrees  Using a 3 by 3 moving window  Measured in the steepest direction of elevation change  Often does not fall parallel to the raster rows or columns  The orientation of a slope  Aspect may be reported as an azimuth angle, measured clockwise in degrees from north.  Identify which locations have the same value.  A useful surface representation.  The hypothetical illumination of a surface by determining illumination values for each cell in a raster.  It can greatly enhance the visualization of a surface for analysis or graphical display, especially when using transparency. Azimuth (angle of the light source = 315° Altitude angle of the light source above the horizon = 45° Azimuth = 180° Altitude = 30° Azimuth = 135° Azimuth = 135° Azimuth = 45° Altitude = 80° Altitude = 45° Altitude = 45°  Parameters: Altitude Azimuth Default sun altitude for hillshade is 45º Default sun azimuth (direction) for hillshade is 315º  A viewshed identifies the cells in an input raster that can be seen from one or more observation locations.  Input: DEM + observer points feature class (points or lines) The nodes and vertices of lines will be used as observation points.  Output: Each cell in the output raster receives a value that indicates how many observer points can be seen from each location. All cells that cannot see the observer point are given a value of 0.  Examples:  Which areas can be seen from a fire lookout tower that is 15 meters high?  From which locations on the landscape will an ancient fortress be visible?  How frequently can a proposed disposal site be seen from an existing highway?  Hydrology toolset:  Flow direction Flow accumulation Stream Network Watershed boundary  Reclassify tool allows you to reclassify or change input cell values to alternative values.  Most common reasons for reclassifying data: Group certain values together Reclassify values to a common scale Replace values based on new information Set specific values to NoData or set NoData cells to a value 40 41  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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