1. Geographic Information Systems Applications in Natural Resource Management Chapter 14 Raster GIS Database Analysis II Michael G. Wing & Pete Bettinger

2. Chapter 14 Objectives The potential applications of raster data for natural resource problem analysis, How distance functions can be applied to raster data, The types of statistical summary search functions for raster data, The capabilities and applications of density operations, Raster data reclassification and map algebra processes, and Data structure conversion considerations.

3. Specific examples of raster analysis Distance functions Statistical summary search functions Density functions Raster reclassification Raster map algebra Database structure conversions

4. Distance functions Calculate the distance to features of interest Distances can be calculated through several methods  Straight line  Allocation distance What is the closest area (similar to Thiessen polygon)  Cost weighted distance Weights are assigned that can be used to take into account slopes, surface materials, or other landscape values  Shortest path How to most efficiently go from one location to another given an existing transportation network

5. Potential applications of distance functions Figure 14.1. Water sources in and around the Brown Tract.

6. Straight line distance Figure 14.2. Straight line distance categories to nearest water source in the Brown Tract.

7. Cost weighted slope values Figure 14.4. Cost weighted slope values to water sources in the Brown Tract.

8. Statistical summary search functions Looking to pull information from other databases based on search areas The search area can be  An individual raster cell  A neighborhood of cells  A zonal statistic where cells of a certain value range or used to return data Described in Chapter 13 Information in other databases that falls within the search area is returned

9. Density functions Determines the intensity or frequency with which something occurs across a landscape or portion of the landscape Use to describe  Road density within a forest  Relative quality of habitat areas given the presence of factors related to habitat conditions  Demonstrating groupings or “hot spots” of activity or landscape feature

10. Density function use Density functions allow us to create maps of intensity values that make it much easier to analyze physical and social settings, particularly when databases contain thousands or more of features Density functions usually allow a GIS operator to select a database feature (number of points) or attribute value (height of trees) to support creation of a density surface

11. Simple density surface Figure 14.5. Simple density surface for roads in the Brown Tract using a 2,000 ft search radius.

12. Raster reclassification The changing of raster database values Why?  Values within a raster may have been updated through additional data collection.  Numerical values are needed instead of current values that are described using categorical or nominal values.  A more detailed description of categorical raster values may be desired.  Raster values may need to be rescaled in order to support raster analyses.

13. Raster reclassification Leads to the creation of a new raster database that contains the altered values

14. Raster map algebra Raster map algebra is the application of mathematical functions to one or more raster databases  The values in a raster database can be added, subtracted, multiplied, or divided by some constant  Multiple rasters can also be added, subtracted, multiplied, or divided by each other Results in a new raster database

15. Raster map algebra applications Divide a raster by some constant  Convert measurement units Add all values within a group of raster databases to determine the sum of an attribute  Use monthly rainfall measurements to calculate an annual rainfall Involve multiple raster databases and multiple mathematical operators  Calculate the velocity of water flowing from a particular point on the landscape

16. Database structure conversions Going from raster to vector or vice versa Why?  Supporting GIS processes that only accommodate vector data,  Supporting GIS processes that only accommodate raster data,  Sharing data with a colleague who can only access one data structure type,  Meeting the data requirements of a client or funding organization, and  Database storage size considerations.

17. Database structure conversions It is inevitable that you will have to consider a data structure conversion if you use GIS to any great extent for project work When converting from vector to raster, you’ll typically be prompted for a specific attribute to carry in the transformation Raster databases can come in different formats  Integer: can handle multiple attributes  Floating point: only one numeric attribute Raster databases can also be changed in terms of resolution  This is know as resampling  Affects database size

18. Application: Most efficient route Brown Tract staff would like to take rocky fill material from rock pits and transport it to the southeast entrance of the Brown Tract for use in trail construction Figure 14.6. Rock pit, southeast entrance, and road system in the Brown Tract.

19. Most efficient route considerations Minimize impact on forest roads  Surface, distance, slope Roads database  Paved  Rocked  Dirt (unpaved)  Also provides distances between locations Slopes: 10%  Derived from DEM Assign numeric values to the raster databases to represent decision variables

20. GIS process Roads GIS database (vector) Conversion to raster database Streams GIS database (raster) Slope GIS database (raster) Combine values Best path GIS database (vector) Rock pit GIS database (vector) Best path algorithm Reclassify road type Reclassify road type Develop cost path & cost direction (raster) Figure 14.7. A general process to identify the shortest path between two locations on the Brown Tract.

21. Results: Most efficient route Time to get to work! Figure 14.8. Shortest path between rock pit and southeast entrance of the Brown Tract given cost weights for road surface and road slope.

22. Application: Density of trees per acre Density functions are designed to tell us about relative abundance or strength of landscape features and attributes Useful, and sometimes necessary, for working with large databases The Brown Tract has an attribute that describes the number of trees per acre within each stand  How does the intensity of trees per acre vary across the forest?  In other words, in which areas would we expect to find the greatest number of trees?

23. Density of trees Why not just look at the values for individual stands?  This ignores the influence of neighboring stands  Would hide “hot spots” of tree density The neighborhood search radius of density functions can be altered to look farther or look closer from features of interest  Stand polygons are the feature of interest  The stand polygon centroids will form the basis of the radius

24. Results: Simple density surface Figure 14.9. Simple density surface for number of trees per acre based on a 1,000 ft search radius.

25. Results: Smoothed density surface 14.10. Smoothed density surface for number of trees per acre based on a 1,000 ft search radius.