Essentials of Geographic Information Systems Chapter 4 Data Models for GIS

Chapter Data Models for GIS In order to visualize natural phenomena , one must determine how to best represent geographic space . Data models are a set of rules or constructs used to describe and represent aspects of the real world in a computer Two primary data models are available to complete this task raster data models and vector data models URL books 75

Raster Data Models LEARNING OBJECTIVE . The objective of this section is to understand how raster data models are implemented in GIS applications . The raster data model is widely used in applications ranging far beyond geographic information systems ( Most likely , you are already very familiar with this data model if you have any experience with digital photographs . The ubiquitous PEG , and TIFF file formats ( among others ) are based on the raster data model ( see Chapter Data Management , Section File Formats ) Take a moment to View your favorite digital image . If you zoom deeply into the image , you will notice that it is composed of an array of tiny square pixels ( or picture elements ) Each of these uniquely colored pixels , when Viewed as a whole , combines to form a coherent image ( Figure Digital Picture with Zoomed Inset Showing Pixilation of Raster Image ) Figure Digital Picture with Zoomed Inset Showing Pixilation Image Furthermore , all liquid crystal display ( computer monitors are based on raster technology as they are composed of a set number of rows and columns of pixels . Notably , the foundation of this technology predates computers and digital cameras by nearly a century . The neoimpressionist artist , Georges , developed a painting technique referred to as pointillism in the , which similarly relies on the amassing of small , monochromatic dots of ink that combine to form a larger image ( Figure Pointillist Artwork ) If you are as generous as the author , you may indeed think of your raster creations as sublime works of art . URL books ) 76

Figure Artwork The raster data model consists of rows and columns of equally sized pixels interconnected to form a planar surface . These pixels are used as building blocks for creating points , lines , areas , networks , and surfaces ( Chapter Map Anatomy , Figure Map Overlay Process illustrates how a land parcel can be converted to a raster representation ) Although pixels may be triangles , or even , square pixels represent the simplest geometric form with which to work . Accordingly , the vast majority of available raster GIS data are built on the square pixel ( Figure Common Raster Graphics Used in GIS Applications Aerial Photograph ( left ) and DEM ( right ) These squares are typically reformed into rectangles of various dimensions if the data model is transformed from one projection to another ( from State Plane coordinates to Universal Transverse coordinates ) URL books 77

Figure Common Raster Graphics Used in Aerial Photograph ( left ) and DEM ( right ) a . Source Data available from Geological Survey , Earth Resources Observation and Science ( EROS ) Center , Sioux Falls , Because of the reliance on a uniform series of square pixels , the raster data model is referred to as a system . Typically , a single data value will be assigned to each grid locale . Each cell in a raster carries a single value , which represents the characteristic of the spatial phenomenon at a location denoted by its row and column . The data type for that cell value can be either integer or ( Chapter Data Management , Section Geographic Data Acquisition ) Alternatively , the raster graphic can reference a database management system wherein attribute tables can be used to associate multiple data values to each pixel . The advance of computer technology has made this second methodology increasingly feasible as large are no longer constrained by computer storage issues as they were previously . URL books 78

The raster model will average all values within a given pixel to yield a single value . Therefore , the more area covered per pixel , the less accurate the associated data values . The area covered by each pixel determines resolution of the raster model from which it is derived . Specifically , resolution is determined by measuring one side of the square pixel . A raster model with pixels representing 10 In by 10 In ( or 100 square meters ) in the real world would be said to have a spatial resolution of 10 In a raster model with pixels measuring by ( square kilometer ) in the real world would be said to have a spatial resolution of and so forth . Care must be taken when determining the resolution of a raster because using an overly coarse pixel resolution will cause a loss of information , whereas using overly pixel resolution will result in significant increases in size and computer processing requirements during display analysis . An effective pixel resolution will take both the map scale and the minimum mapping unit of the other GIS data into consideration . In the case of raster graphics with coarse spatial resolution , the data values associated with specific locations are not necessarily explicit in the raster data model . For example , if the location of telephone poles were mapped on a coarse raster graphic , it would be clear that the entire cell would not be by the pole . Rather , the pole would be assumed to be located somewhere within that cell ( typically at the center ) Imagery employing the raster data model must exhibit several properties . First , each pixel must hold at least one value , even if that data value is zero . Furthermore , if no data are present for a given pixel , a data value placeholder must be assigned to this grid cell . Often , an arbitrary , readily identifiable value ( will be assigned to pixels for which there is no data value . Second , a cell can hold any alphanumeric index that represents an attribute . In the case of quantitative , attribute assignation is fairly straightforward . For example , if a raster image denotes elevation , the data values for each pixel would be some indication of elevation , usually in feet or meters . In the case of qualitative , data values are indices that necessarily refer to some predetermined translational rule . In the case of a raster graphic , the following rule may be applied grassland , agricultural , disturbed , and so forth ( Figure Cover Raster Image ) The third property of the raster data model is that points and lines move to the center of the cell . As one might expect , if a resolution raster image contains a river or URL books 79

stream , the location of the actual waterway within the river pixel will be unclear . Therefore , there is a general assumption that all ( point ) and ( line ) features will be located toward the center of the cell . As a corollary , the minimum width for any line feature must necessarily be one cell regardless of the actual width of the feature . If it is not , the feature will not be represented in the image and will therefore be assumed to be absent . Figure Raster Image Source Data available from Geological Survey , Earth Resources Observation and Science ( EROS ) Center , Sioux Falls , URL books

Several methods exist for encoding raster data from scratch . Three of these models are as follows . raster encoding . This minimally intensive method encodes a raster by creating records for each cell value by row and column ( Figure Encoding of Raster Data ) This method could be thought of as a large spreadsheet wherein each cell of the spreadsheet represents a pixel in the raster image . This method is also referred to as exhaustive . raster encoding . This method encodes cell values in runs of similarly valued pixels and can result in a highly compressed image ( Figure Encoding of Raster Data ) The encoding method is useful in situations where large groups of neighboring pixels have similar values ( discrete such as land cover or habitat suitability ) and is less useful where neighboring pixel values vary widely ( continuous such as elevation or temperatures ) raster encoding . This method divides a raster into a hierarchy of quadrants that are subdivided based on similarly valued pixels ( Figure Encoding of Raster Data ) The division of the raster stops when a quadrant is made entirely from cells of the same value . A quadrant that can not be subdivided is called a leaf Figure ( Encoding ( 00001000 00001100 00011100 00011100 ROWS 01111100 01111110 01111110 00000000 URL books ( 81

Figure ofR ( II Row II II II II II ROM Figure ( ua ( Encoding Yellow Leaf Node White Leaf Node ( 211 , 213 , 220 , 231 ) 300 , 301 , 302 , URL books ( 56 46 46 26 27 27 82 of the Raster Model The use of a raster data model confers many advantages . First , the technology required to create raster graphics is inexpensive and ubiquitous . Nearly everyone currently owns some sort of raster image generator , namely a digital camera , and few cellular phones are sold today that don include such functionality . Similarly , a plethora of satellites are constantly beaming raster graphics to facilities across the globe ( Chapter Data Management , Section File Formats ) These graphics are often posted online for private or public use , occasionally at no cost to the user . Additional advantages of raster graphics are the relative simplicity of the underlying data structure . Each grid location represented in the raster image correlates to a single value ( or series of values if attributes tables are included ) This simple data structure may also help explain why it is relatively easy to perform overlay analyses on raster data ( for more on overlay analyses , see Chapter Analysis Vector Operations , Section Single Layer Analysis ) This simplicity also lends itself to easy interpretation and maintenance of the graphics , relative to its vector counterpart . Despite the advantages , there are also several disadvantages to using the raster data model . The disadvantage is that raster files are typically very large . Particularly in the case of raster images built from the encoding methodology , the sheer number of values stored for a given result in potentially enormous . Any raster file that covers a large area and has somewhat finely resolved pixels will quickly reach hundreds of megabytes in size or more . These large are only getting larger as the quantity and quality of raster continues to keep pace with quantity and quality of computer resources and raster data collectors ( digital cameras , satellites ) A second disadvantage of the raster model is that the output images are less pretty than their vector counterparts . This is particularly noticeable when the raster images are enlarged or zoomed ( refer to Figure Digital Picture with Zoomed Inset Showing Pixilation of Raster Image ) Depending on how far one zooms into a raster image , the details and coherence of that image will quickly be lost amid a pixilated sea of seemingly randomly colored grid cells . URL books 83

The geometric transformations that arise during map efforts can cause problems for raster graphics and represent a third disadvantage to using the raster data model . As described in Chapter Map Anatomy , Section Map Scale , Coordinate Systems , and Map Projections , changing map projections will alter the size and shape of the original input layer and frequently result in the loss or addition of pixels ( White 2006 ) These alterations will result in the perfect square pixels of the input layer taking on some alternate rhomboidal dimensions . However , the problem is larger than a simple reformation of the square pixel . Indeed , the of a raster image from one projection to another brings change to pixel values that may , in turn , alter the output information ( Seong 2003 ) The disadvantage of using the raster data model is that it is not suitable for some types of spatial analyses . For example , difficulties arise when attempting to overlay and analyze multiple raster graphics produced at differing scales and pixel resolutions . Combining information from a raster image with 10 spatial resolution with a raster image with spatial resolution will most likely produce nonsensical output information as the scales of analysis are far too disparate to result in meaningful or interpretable conclusions . In addition , some network and spatial analyses ( determining directionality or ) can be problematic to perform on raster data . KEY TAKEAWAYS Raster data are derived from a system of contiguous cells containing specific attribute information . The spatial resolution of a raster represents a measure of the accuracy or detail of the displayed information . The raster data model is widely used by technologies such as digital and monitors . Care should be taken to determine whether the raster or vector data model is best suited for your data analytical needs . EXERCISES . Examine a digital photo you have taken recently . Can you estimate its spatial resolution ?

URL books 84 . If you were to create a raster data file showing the major types in your county , which encoding method would you use ?

What method would you use if you were to encode a map of the major waterways in your county ?

Why ?

White , 2006 . Display of Pixel Loss and Replication in Raster Data from the Sinusoidal International 21 ( Seong , 2003 . Modeling the Accuracy of Image Data Sensing 24 ( 11 ) URL books 85 Vector Data Models LEARNING OBJECTIVE . The objective of this section is to understand how vector data models are implemented in GIS applications . In contrast to the raster data model is the vector data model . In this model , space is not into discrete grid cells like the raster model . Vector data models use points and their associated , coordinate pairs to represent the vertices of spatial features , much as if they were being drawn on a map by hand ( 1989 ) The data attributes of these features are then stored in a separate database management system . The spatial information and the attribute information for these models are linked via a simple number that is given to each feature in a map . Three fundamental vector types exist in geographic information systems ( points , lines , and ( Figure Points , Lines , and ) Points are objects that contain only a single coordinate pair . Points are typically used to model singular , discrete features such as buildings , wells , power poles , sample locations , and so forth . Points have only the property of location . Other types of point features include the node and the vertex . a point is a alone feature , while a node is a topological junction representing a common , coordinate pair between intersecting lines . Vertices are as each bend along a line or polygon feature that is not the intersection of lines or . Figure Points , Lines , and URL books 85

Points can be spatially linked to form more complex features . Lines are features composed of multiple , explicitly connected points . Lines are used to represent linear features such as roads , streams , faults , boundaries , and so forth . Lines have the property of length . Lines that directly connect two nodes are sometimes referred to as chains , edges , segments , or arcs . are features created by multiple lines that loop back to create a closed feature . In the case of , the first coordinate pair ( point ) on the first line segment is the same as the last coordinate pair on the last line segment . are used to represent features such as city boundaries , geologic formations , lakes , soil associations , Vegetation communities , and so forth . have the properties of area and perimeter . are also called areas . Vector Data Models Structures Vector data models can be structured many different ways . We will examine two of the more common data structures here . The simplest vector data structure is called the spaghetti data model ( 1982 ) In the spaghetti model , each point , line , or polygon feature is represented as a string of , coordinate pairs ( or as a single , coordinate pair in the case of a vector image with a single point ) with no inherent structure ( Figure Spaghetti Data Model ) One could envision each line in this model to be a single strand of spaghetti that is formed into complex shapes by the addition of more and more strands of spaghetti . It is notable that in this model , any that lie adjacent to each other must be made up of their own lines , or stands of spaghetti . In other words , each polygon must be uniquely defined by its own set of , coordinate pairs , even if the adjacent share the exact same boundary information . This creates some redundancies within the data model and therefore reduces efficiency . URL books 87

Figure ( ofthe point and the number of vertex ) ofthe line and number of vertex ) of polygon and number of vertex ) coordinates of the vertex ) coordinates of the vertex again ) Despite the location designations associated with each line , or strand of spaghetti , spatial relationships are not explicitly encoded within the spaghetti model rather , they are implied by their location . This results in a lack of topological information , which is problematic if the user attempts to make measurements or analysis . The computational requirements , therefore , are very steep if any advanced analytical techniques are employed on vector files structured thusly . Nevertheless , the simple structure of the spaghetti data model allows for efficient reproduction of maps and graphics as this topological information is unnecessary for plotting and printing . In contrast to the spaghetti data model , the topological data model is characterized by the inclusion of topological information within the , as the name implies . Topology is a set of rules that model the relationships between neighboring points , lines , and and determines how they share geometry . For example , consider two adjacent . In the spaghetti model , the shared boundary of two neighboring is defined as two separate , identical lines . The inclusion of topology into the data model allows for a single line to represent this shared boundary with an explicit reference to denote which side of the line belongs with which polygon . Topology is also concerned with preserving spatial properties when the forms are bent , stretched , or placed under similar geometric transformations , which allows for more efficient projection and of map . URL books 88

Three basic topological precepts that are necessary to understand the topological data model are outlined here . First , connectivity describes the topology for the feature . As discussed previously , nodes are more than simple points . In the topological data model , nodes are the intersection points where two or more arcs meet . In the case of topology , arcs have both a ( starting node ) indicating where the arc begins and a ( ending node ) indicating where the arc ends ( Figure Topology ) In addition , between each node pair is a line segment , sometimes called a link , which has its own number and references both its and . In Figure Topology , arcs , and all intersect because they share node 11 . Therefore , the computer can determine that it is possible to move along are and turn onto are , while it is not possible to move from are to are , as they do not share a common node . Figure ( Ie Topology List A The second basic topological precept is area . Area definition states that an arc that connects to surround an area defines a polygon , also called topology . In the case of topology , arcs are used to construct , and each arc is stored only once ( Figure Topology ) This results in a reduction in the amount of data stored and ensures that adjacent polygon URL books 89

boundaries do not overlap . In the Figure Topology , the topology makes it clear that polygon is made up of arcs , and 10 . Figure Topology Polygon Arc List 12 10 11 Contiguity , the third topological precept , is based on the concept that that share a boundary are deemed adjacent . polygon topology requires that all arcs in a polygon have a direction ( a and a ) which allows adjacency information to be determined ( Figure Polygon Topology ) that share an arc are deemed adjacent , or contiguous , and therefore the left and right side of each arc can be defined . This left and right polygon information is stored explicitly within the attribute information of the topological data model . The universe polygon is an essential component of polygon topology that represents the external area located outside of the study area . Figure Polygon Topology shows that arc is bound on the left by polygon and to the right by polygon Polygon A , the universe polygon , is to the left of arcs , and . URL books 90

Figure Pol ) on ) A Topology Left Right A Polygon Polygon na 10 11 Topology allows the computer to rapidly determine and analyze the spatial relationships of all its included features . In addition , topological information is important because it allows for error detection within a vector . In the case of polygon features , open or unclosed , which occur when an arc does not completely loop back upon itself , and unlabeled , which occur when an area does not contain any attribute information , violate topology rules . Another topological error found with polygon features is the sliver . Slivers occur when the shared boundary of two do not meet exactly ( Figure Common Topological Errors ) In the case of line features , topological errors occur when two lines do not meet perfectly at a node . This error is called an undershoot when the lines do not extend far enough to meet each other and an overshoot when the line extends beyond the feature it should connect to ( Figure Common Topological Errors ) The result of and is a dangling node at the end of the line . Dangling nodes aren always an error , however , as they occur in the case of streets on a road map . URL books 91

Figure ( Open Polygon Undershoot overshoot Many types of spatial analysis require the degree of organization offered by explicit data models . In particular , network analysis ( finding the best route from one location to another ) and measurement ( the length of a river segment ) relies heavily on the concept of and nodes and uses this information , along with attribute information , to calculate distances , shortest routes , quickest routes , and so forth . Topology also allows for sophisticated neighborhood analysis such as determining adjacency , clustering , nearest neighbors , and so forth . Now that the basics of the concepts of topology have been outlined , we can begin to better understand the topological data model . In this model , the node acts as more than just a simple point along a line or polygon . The node represents the point of intersection for two or more arcs . Arcs may or may not be looped into . Regardless , all nodes , arcs , and are individually numbered . This numbering allows for quick and easy reference within the data model . Disadvantages of the Vector Model URL books 92

In comparison with the raster data model , vector data models tend to be better representations of reality due to the accuracy and precision of points , lines , and over the regularly spaced grid cells of the raster model . This results in vector data tending to be more aesthetically pleasing than raster data . Vector data also provides an increased ability to alter the scale of observation and analysis . As each coordinate pair associated with a point , line , and polygon represents an exact location ( albeit limited by the number of digits or data acquisition ) zooming deep into a vector image does not change the View of a vector graphic in the way that it does a raster graphic ( see Figure Digital Picture with Zoomed Inset Showing Pixilation of Raster Image ) Vector data tend to be more compact in data structure , so sizes are typically much smaller than their raster counterparts . Although the ability of modern computers has minimized the importance of maintaining small sizes , vector data often require a fraction the computer storage space when compared to raster data . The advantage of vector data is that topology is inherent in the vector model . This topological information results in spatial analysis ( error detection , network analysis , proximity analysis , and spatial transformation ) when using a vector model . Alternatively , there are two primary disadvantages of the vector data model . First , the data structure tends to be much more complex than the simple raster data model . As the location of each vertex must be stored explicitly in the model , there are no shortcuts for storing data like there are for raster models ( the and encoding ) Second , the implementation of spatial analysis can also be relatively complicated due to minor differences in accuracy and precision between the input . Similarly , the algorithms for manipulating and analyzing vector data are complex and can lead to intensive processing requirements , particularly when dealing with large . KEY TAKEAWAYS Vector data utilizes points , lines , and to represent the spatial features in a map . URL books 93

Topology is an informative property that describes the connectivity , area definition , and contiguity of interrelated points , lines , and polygon . Vector data may or may not be explicit , depending on the file data structure . Care should be taken to determine whether the raster or vector data model is best suited for your data analytical needs . EXERCISES . What vector type ( point , line , or polygon ) best represents the following features state boundaries , telephone poles , buildings , cities , stream networks , mountain peaks , soil types , flight tracks ?

Which of these features can be represented by multiple vector types ?

What conditions might lead you choose one vector type over another ?

Draw a point , line , and polygon feature on a simple Cartesian coordinate system . From this drawing , create a spaghetti data model that approximates the shapes shown therein . Draw three adjacent on a simple Cartesian coordinate system . From this drawing , create a topological data model that incorporates , and polygon topology . 1989 . Geographic Information A Management Perspective . Canada Publications . 1982 . A Classification of Software Components Commonly Used in Geographic Information Systems . In Proceedings of the Workshop on the Design and Implementation of Geographic Information Systems , Honolulu , HI . URL books 94

Satellite Imagery and Aerial Photography LEARNING OBJECTIVE . The objective of this section is to understand how satellite imagery and aerial photography are implemented in GIS applications . A wide variety of satellite imagery and aerial photography is available for use in geographic information systems ( Although these products are basically raster graphics , they are substantively different in their usage within a GIS . Satellite imagery and aerial photography provide important contextual information for a GIS and are often used to conduct digitizing ( Chapter Data Management , Section Secondary Data Capture ) whereby features from the image are converted into vector . Satellite Imagery Remotely sensed satellite imagery is becoming increasingly common as satellites equipped with technologically advanced sensors are continually being sent into space by public agencies and private companies around the globe . Satellites are used for applications such as military and civilian earth observation , communication , navigation , weather , research , and more . Currently , more than satellites have been sent to space , with over of them originating from Russia and the United States . These satellites maintain different altitudes , inclinations , eccentricities , and orbital centers , allowing them to image a wide variety of surface features and processes ( Figure Satellites Orbiting the Earth ) URL books 95

Figure Satellites can be active or passive . Active satellites make use of remote sensors that detect responses from objects that are irradiated from artificially generated energy sources . For example , active sensors such as radars emit radio waves , laser sensors emit light waves , and sonar sensors emit sound waves . In all cases , the sensor emits the signal and then calculates the time it takes for the returned signal to bounce back from some remote feature . Knowing the speed of the emitted signal , the time delay from the original emission to the return can be used to calculate the distance to the feature . Passive satellites , alternatively , make use of sensors that detect the or emitted electromagnetic radiation from natural sources . This natural source is typically the energy from the sun , but other sources can be imaged as well , such as magnetism and geothermal activity . Using an example we ve all experienced , taking a picture with a camera would be active remote sensing , while using a camera without a ( relying on ambient light to illuminate the scene ) would be passive remote sensing . The and quantity of satellite imagery is largely determined by their resolution . There are types of resolution that characterize any particular remote sensor ( Campbell 2002 ) The spatial resolution of URL books 96

a satellite image , as described previously in the raster data model section ( Section Raster Data Models ) is a direct representation of the ground coverage for each pixel shown in the image . If a satellite produces imagery with a 10 resolution , the corresponding ground coverage for each of those pixels is 10 by 10 , or 100 square meters on the ground . Spatial resolution is determined by the sensors instantaneous of view ( The is essentially the ground area through which the sensor is receiving the electromagnetic radiation signal and is determined by height and angle of the imaging platform . Spectral resolution denotes the ability of the sensor to resolve wavelength intervals , also called bands , within the electromagnetic spectrum . The spectral resolution is determined by the interval size of the wavelengths and the number of intervals being scanned . Multispectral and hyperspectral sensors are those sensors that can resolve a multitude of wavelengths intervals within the spectrum . For example , the satellite resolves images for bands at the blue ( green ( red ( and ( wavelength intervals on its multispectral sensor . Temporal resolution is the amount of time between each image collection period and is determined by the repeat cycle of the satellite orbit . Temporal resolution can be thought of as or . Areas considered are those located directly beneath the sensor while areas are those that are imaged obliquely . In the case of the satellite , the temporal resolution is to days for imaging and 144 days for imaging . The fourth and final type of resolution , radiometric resolution , refers to the sensitivity of the sensor to variations in brightness and specifically denotes the number of levels that can be imaged by the sensor . Typically , the available radiometric values for a sensor are ( yielding values that range from as 256 unique values or as ' values ) or ( see Chapter Data , Section Data Types for more on bits ) for example , maintains resolution for its bands and can therefore record values for each pixel that range from to 255 . Because of the technical constraints associated with satellite remote sensing systems , there is a between these different types of resolution . Improving one type of resolution often necessitates a URL books 97

reduction in one of the other types of resolution . For example , an increase in spatial resolution is typically associated with a decrease in spectral resolution , and vice versa . Similarly , geostationary satellites ( those that circle the earth proximal to the equator once each day ) yield high temporal resolution but low spatial resolution , while satellites ( those that synchronize a orbit of the sensor with the sun illumination ) yield low temporal resolution while providing high spatial resolution . Although technological advances can generally improve the various resolutions of an image , care must always be taken to ensure that the imagery you have chosen is adequate to the represent or model the features that are most important to your study . Aerial Photography Aerial photography , like satellite imagery , represents a vast source of information for use in any GIS . Platforms for the hardware used to take aerial photographs include airplanes , helicopters , balloons , rockets , and so forth . While aerial photography connotes images taken of the visible spectrum , sensors to measure bands within the spectrum ( ultraviolet , infrared , can also be to aerial sources . Similarly , aerial photography can be active or passive and can be taken from vertical or oblique angles . Care must be taken with aerial photographs as the sensors used to take the images are similar to cameras in their use of lenses . These lenses add a curvature to the images , which becomes more pronounced as one moves away from the center of the photo ( Figure Curvature Error Due to Lenticular Properties of Camera ) URL books 98

Figure ' Error Due to PI ' Another source of potential error in an aerial photograph is relief displacement . This error arises from the aspect of terrain features and is seen as apparent leaning away of vertical objects from the center point of an aerial photograph . To imagine this type of error , consider that a smokestack would look like a doughnut if the viewing camera was directly above the feature . However , if this same smokestack was observed near the edge of the camera view , one could observe the sides of the smokestack . This error is frequently seen with trees and multistory buildings and worsens with increasingly taller features . are vertical photographs that have been geometrically corrected to remove the curvature and error from images ( Figure ) The most common product is the digital ortho quarter quadrangle ( are available through the US Geological Survey ( who began producing these images from their library of National Aerial Photography Program photos . These images can be obtained in either or color with URL books 99

spatial resolution and radiometric resolution . As the name suggests , these images cover a quarter of a minute quadrangle , which equals an approximately 25 square mile area . Included with these photos is an additional 50 to edge around the photo that allows users to mosaic many into a single , continuous image . These are ideal for use in a GIS as background display information , for data editing , and for digitizing . Figure URL books ) a 100

Source Data available from US . Geological Survey , Earth Resources Observation and Science ( EROS ) Center , Sioux Falls , KEY TAKEAWAYS Satellite imagery is a common tool for GIS mapping applications as this data becomes increasingly available due to ongoing technological advances . Satellite imagery can be passive or active . The four types of resolution associated with satellite imagery are spatial , spectral , temporal , and radiometric . Vertical and oblique aerial photographs provide valuable baseline information for GIS applications . EXERCISE . Go to the website ( and download two satellite images of the area in which you reside . What are the different spatial , spectral , temporal , and radiometric resolutions for these two images ?

Do these satellites provide active or passive imagery ( or both ) Are they geostationary or ?

Campbell , 2002 . Introduction to Remote Sensing . New York Press . URL books 101