GIS Portfolio: Ross Wygmans

Land cover is the biophysical description of the earth's surface and is directly observable, whereas land use is the documentation of human uses of the landscape and is not directly observable. There are many uses for Land Use / Land Cover (LULC) classification, such as urban planning, natural resource inventories, Utilities, and many more.  In this lab, we were given a natural color aerial photograph of an area in Pascagoula, MS and asked to create a LULC map based on the aerial imagery. We then generated 30 sample points and used Google Street View to "ground-truth" these points to see if our classifications matched what was observed on the ground. Below is the completed LULC map along with 30 sample points. Green points indicate areas that were mapped correctly, and red points indicate areas that were mapped incorrectly. The overall accuracy of this maps is 66.66%. 

Tone and Texture Identifying features in aerial photographs is not always a simple task. Fortunately, there are several principles and techniques available to analysts that can assist in interpreting aerial photos and help make sense of the images captured. First of all, it is important to be familiar with the various tones and textures one may encounter when interpreting aerial images. The map below highlights areas with five different tones (very light, light, medium, dark, and very dark) and five different textures (very fine, fine, mottled, coarse, and very coarse). Considering the tone and texture of an area or object can be a good starting point when interpreting aerial photographs.  Identifying Features There are also several identifying criteria that can be helpful when interpreting aerial images -- shape and size, shadow, pattern, and association. Shape and size define what an object "looks like", shadows provide an extra angle of vision of objects that cannot be see...

Selecting an interpolation method for sample data can result in vast differences in the surface layer output. In this lab, we compared surfaces derived from four different interpolation methods based on water quality data for Tampa Bay. These interpolation methods are Thiessen polygons (or Nearest Neighbor), Inverse Distance Weighted (IDW), spline regularized, and spline tension.  Below is a screenshot of the surface layer created using the spline tension method. Of the four interpolation methods, I believe this method is the most effective at representing the Biochemical Oxygen Demand (BOD) concentrations throughout Tampa Bay because it most accurately reflects the values of the sample points.  

Triangulated irregular networks, or TINs, are a vector-based models used to represent surface morphology. Vertices (or points) are triangulated using various interpolation methods and are connected by a series of edges. Unlike raster-based digital elevation models, or DEMs, nodes can be placed irregularly over a surface, which can contribute to finer detail in more elevationally complex areas. The image below represents an area near Big Bear Lake, CA. The slope of each triangle within the TIN is symbolized using graduated colors -- triangles with the greatest slope are shown in dark purple, and triangles with less slop are shown in light purple. The horizontal lines crossing the landscape are contour lines. The white lines represent regular contours, while the blue line represents an index contour. 

Data completeness is another important metric used to assess data quality. In this week's lab, we compared two sets of road networks for Jackson County, Oregon. The first dataset was from the county itself, and the second dataset was from the TIGER 2000 data. Since there is no standard methodology for measuring data completeness, we used total length of each road network as a proxy for data completeness.  We first divided the county into a 1km by 1km grid so that each grid cell could be analyzed independently, thereby showing the distribution of data completeness across the entire county. Each road network was divided along the grid lines to facilitate the analysis of total road length within each individual grid cell. We then calculated the percent difference between the two road networks using the county dataset as the baseline.  The map below shows the results of this analysis. The TIGER 2000 road network was more complete than the county network in 162 of 297 total grid po...

In this lab, we were asked to compare the horizontal accuracy of two street datasets for the city of Albuquerque, NM. One of the datasets came from the city itself, and the other came from StreetMap USA. Below is a general outline of the process followed.   Selected 20 test points for both datasets evenly distributed throughout the study area. These test points were located at clearly defined intersections.  Created a reference for each test point based on orthomosaic images. Generated XY coordinate data for test points and reference points and calculated accuracy statistics using the following table.  Figure from the Positional Accuracy Handbook. 1999. Minnesota Planning, Land Management Information Center, St. Paul, MN The map below shows the location of the 20 test points selected.  Results City of Albuquerque Dataset Tested 13.206 feet horizontal accuracy at 95% confidence interval.  StreetMap USA Dataset Tested 241.367 feet horizontal accuracy at 95% c...

The ability to describe, quantify, and understand spatial data quality is an essential function of all GIS practitioners and professionals. Two of the most common metrics used to describe spatial data quality are accuracy and precision.  The terms accuracy and precision are sometimes used interchangeably. While these metrics of spatial data quality are certainly related, these two terms describe two distinct attributes. Accuracy refers to how close a mapped representation of an object is to the object's actual location, and precision refers to the consistency of a measurement method.  Precision The map below shows 50 waypoints that were collected at a single location using a Garmin GPSMAP 76 unit. The yellow star represents the average waypoint, which is the average XY coordinates of all 50 waypoints. The buffers extending out from the average waypoint represent the 50th, 68th, and 95th percentiles. These areas contain 50, 68, and 95 percent of all the waypoints taken. The m...