Geographic Information Systems What is a Geographic Information

Geographic Information Systems

What is a Geographic Information System (GIS)? • A GIS is a particular form of Information System applied to geographical data. • An Information System is a set of processes, executed on raw data to produce information which will be useful when making decisions. • A system is a group of connected entities and activities which interact for a common purpose. This discussion is derived from a seminar by Dr. David Waits, SST Development Group, Inc. , Stillwater

What is a Geographic Information System (GIS)? • An information system has a full range of functions to: – – – process observations process measurements provide descriptions explain data make forecasts make decisions

What is a Geographic Information System (GIS)? • In a geographic information system, information is characterized spatially. • In a GIS the common purpose is decision making to manage: – – – land resources transportation retailing OR any other spatially distributed activity

What is a Geographic Information System (GIS)? • A GIS is an organized collection of computer hardware, software, geographic data, and personnel to efficiently capture, store, update, manipulate, analyze, and display all forms of geographically referenced information. • A GIS integrates spatial and other kinds of information within a single system to provide a consistent framework for analyzing geographic (spatial) data.

What is a Geographic Information System (GIS)? • A GIS makes connections between activities based on geographic proximity. • The digital data structure can be conceptualized as a set of “floating electronic maps” with a common registration allowing the used to “look” down (drill down) and across the stack of maps.

What is a Geographic Information System (GIS)? • The spatial relationships can be summarized (data base inquiries) or manipulated (analytical processing). • Another definition of GIS - An internally referenced, automated, spatial information system for data mapping, management, and analysis

GIS Process Capture Data Convert Data to Digital Format Store Data in Computer Process Data Register Map Base Interpret Data Display Results

Images Image Processing System Statistical Reports Map Digitizing System Statistical Analysis System Spatial Attribute Data Base Database Management System Geographic Analysis System GIS System Cartographic Display System Maps Statistics Tabular Data

GIS - Map Stacking NDVI From Aerial Image Nitrogen Availability Estimate from Aerial Photo p. H Layer Geographic Information System Courtesy of PPI

“Drilling Down” Through The Data Layers Courtesy of PPI

GIS Data Formats • There are two formats used by GIS systems to store and retrieve geographical data: – Raster – Vector

Raster Format • Data are divided into cell, pixels, or elements • Cells are organized in arrays • Each cell has a single value • Row and Column Numbers are used to identify the location of the cell within the array. • Perhaps the most common example of raster data is a digital image.

Vector Format • Data are associated with points, lines, or boundaries enclosing areas • Points are located by coordinates • Lines are described by a series of connecting vectors (line segments described by the coordinates of the start of the vector, its direction, and magnitude or length). • Areas or polygons are described by a series of vectors enclosing the area.

Vector Format • Any number of factors or attributes can be associated with a point, line, or polygon. • Data are stored in two files: – a file containing location information – a file containing information on the attributes • A third file contains information needed to link positional data with their attributes.

Vector and Raster Representation of Point Map Features Map Feature GIS Vector Format (X, Y) Coordinate in space GIS Raster Format Cell Located in an Array

Vector and Raster Representation of Line Map Features Map Feature GIS Vector Format GIS Raster Format

Vector and Raster Representation of Area Map Features Map Feature GIS Vector Format GIS Raster Format

Vector and Raster Formats • Most GIS software can display both vector and raster data. • Only a limited number of programs can analyze both types of data or make raster type analyses in vector formats. • “Low end” software may display only raster format and be useful only for creating maps.

Comparison of Raster and Vector Formats Raster • Raster formats are efficient when comparing information among arrays with the same cell size. • Raster files are generally very large because each cell occupies a separate line of data, only one attribute can be assigned to each cell, and cell sizes are relatively small. Vector • Vector formats are efficient when comparing information whose geographical shapes and sizes are different. • Vector files are much smaller because a relatively small number of vectors can precisely describe large areas and a many attributes can be ascribed to these areas.

Comparison of Raster and Vector Formats Raster • Raster representations tend to be relatively coarse and imprecise Vector • Vector representations of shapes can be very precise.

Coordinate Systems • Spatial data are generally recorded as latitude and longitude, frequently as decimal degrees. • Other systems commonly used are the Universal Transverse Mercator (UTM) and State Plane Coordinates. These systems are projections of the curved surface of the globe on to a plane surface.

Latitude and Longitude • Latitude: – Reference datum is the equator – Positive is north & Negative is south • 0 to 90 O • 0 to – 90 O • Longitude – Reference datum is the Prime (Greenwich, UK) Meridian – Positive is east – Negative is west • 0 to 180 O • 0 to – 180 O UTM

Degree Conversion • Degrees, DD, Minutes, MM, Seconds, SS • Decimal Degrees = DD + MM/60 + SS/3600

Coordinate Systems • In UTM, the preferred system, the distance unit is the meter. • The unit of the state plane system is the foot. • There is generally a different coordinate system for each state in the state plane system. • In the UTM system, projections are made in zones of approximately 6 degrees of longitude.

Coordinate Systems • There are two datums (reference planes) commonly used to make projections: • North American Datum of 1927 (NAD 27). This has been replaced by NAD 83. • The World Geographic Reference System of 1984 (WGS 84). The WGS 84 datum can be used world wide. The default datum of many GPS receivers is the WGS 84 datum.

UTM Zones 36. 154 36. 116

UTM Specifications • UTM position is specified by: – Number of the Zone – North (or South) of the equator – East and West of the central meridian of the zone – Distances are in meters • Coordinates are referred to as “Northings”, “Eastings”, etc. – N xxxxxx, E yyyyyy

Interpolation to Predict Missing Data • Frequently, data are collected at discrete points located a significant distance apart or some of the data are missing. • Interpolation is used to predict the values of the missing data. • There a number of interpolation algorithms available in SST Toolbox and other software.

Interpolation Algorithms • • • Nearest neighbor Local Averaging Inverse distance to a power Radial bias functions Shepard’s Method Kriging AND • Simple Contouring ID 2

What is the effect of the interpolation algorithm on the estimate of missing data? Efaw 1 x 1 Experiment - Phosphorus “Missing” Data

Nearest Neighbor Missing Data • Value of the nearest measurement to the missing data. • In the case of values at the same distance, the average of those values Nearest Neighbor

Local Average Missing Data • Average of all values within a predetermined distance. Averaged Values

Inverse (Weighted) Distance Search Radius < 3 ft • Values are weighted by the inverse of their distance from the missing value. The weights can be raised to a power. The interpolated value is equal to the sum of the weighted values divided by the sum of the weights. Missing Data

Inverse (Weighted) Distance Missing Data W=1 W = 0. 707 W = 0. 5 W = 0. 447 W = 0. 354

Missing Values and Predicted Values Phosphorus at Efaw

Error In Predicting Missing Data

Comparison on Interpolation Algorithms Nearest Neighbor Average of Adjacent Elements _______________ 17. 3 Inverse Distance Radius<3 ft % Error ______________ 23. 1 19. 5

Prediction by Linear interpolation Between Every Fifth Data Point Efaw 1 by Experiment

OSU Wheat Pasture Research Station Marshall Oklahoma (March 6, 2006) Custom ATV Green. Seeker Mapper • NDVI (Normalized Difference Vegetative Index, Red and NIR wavelengths) • 0. 418 m 2 (4. 5 ft 2) sensor resolution • 3. 66 m (12 ft) boom width • 14. 63 m (48 ft) paralleled swaths • total sample area = 3. 66/14. 63 = ¼ of field With Paddock Layout

Wheat Pasture Center Scanned with 6 ATV Mounted Green. Seeker Sensors 3/6/2006

OSU Wheat Pasture Research Station Marshall Oklahoma (March 6, 2006) Custom ATV Green. Seeker Mapper • NDVI (Normalized Difference Vegetative Index, Red and NIR wavelengths) • 0. 418 m 2 (4. 5 ft 2) sensor resolution • 3. 66 m (12 ft) boom width • 14. 63 m (48 ft) paralleled swaths • total sample area = 3. 66/14. 63 = ¼ of field With Paddock Layout Raw NDVI Sensor Values

Grid Map Inverse Distance Interpolation Method 7. 32 m X 7. 32 m = 53. 5 m 2 (24 ft X 24 ft = 576 ft 2) Grid Map Final Overlaid Gridwith Map. Raw Values

Paddock 3 shows to have more forage biomass than paddocks 2 and 4. More plant biomass in turn rows because of higher seeding rates. Increased plant biomass in areas where soil moisture is more available due to water holding/runoff characteristics relative to terrain and terrace location. NDVI Relative to Wheat Pasture Forage Variability • NDVI mostly represents plant vigor. Photosynthetic activity, total live plant biomass, plant water stress, etc. can be represented by NDVI with proper calibration techniques. • NDVI variability between paddocks and within paddocks show forage biomass differences exist at least at a 53. 5 m 2 resolution. • Small and large scale variability can be attributed to many factors such as stocking density, soil moisture variability, fertilizer inputs, seeding rate, preferential grazing, and water tank/mineral feeder location. • Quantitative analysis can be conducted on a spatial basis including effects from variability factors and their associated dynamics.