Ch.15 Application GIS

8/6/2019 Ch.15 Application GIS

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GIS APPLICATION

a a a na a

0706173465

Faculty of Engineering

University of Indonesia

REMOTE SENSING: GIS APPLICATION [email protected]


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GIS Software

Relationship between Remotely Sensed and GIS

Contributions of Remote Sensing to GIS

pp ca on o emo e ens ng Conclusion



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Geo ra hic Information S stems GIS are

specialized computer programs designed toanalyze spatially referenced data.

specific geographic region.

These overlays may depict raw data (e.g.opograp c e eva on or may s ow ema c

information (e.g. soils, land use, or geology), butthey must share common geographic qualities

t at perm t t em to e merge so t at one canidentify and analyze interrelationships betweenthe data.



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GIS data consist of many compatible data sets


for the same geographic region


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relationships to each another.Remote sensing systems contribute data to GIS.

Remotely sensed data can provide timely information

at low cost and in a form that is compatible with the

Both GIS and digital remote sensing systems usesimilar e ui ment and similar com uter ro ram.

The non remote sensing data from a GIS can be usedto assist in the analysis of remotely sensed images



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elements:

Computer programs

Operator (personnel that operate and



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A GIS re uires s ecialized ro rams tailored for

the manipulation of geographic data. The tasks essential for GIS:

Overlay capability permits the analyst to superimpose

two or more data sets for display or analysis.

overlays on the screen so that the two pattern can beseen together in a single image.

analyst can define new variables or categories basedupon the matching of different overlays at each point onthe map.



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Visual overla and Lo ical overla



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O erations for GIS:

Data Input Spatial interpolation Display Raster-to-vector conversion

Subset Data output

Overlay Data storage and vertical

Registration/image matching Network operation

Resampling Data manipulation

Logical Operations Data reporting Arithmetic operations Statistical generation


- -


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packages for GIS analysis:

Intergraph Corporation

MapInfo

eograp c esources na ys s

and Support System)



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packages for GIS analysis: . .

Intergraph Corporation (www.integraph.com)

. .

MapInfo (www.mapinfo.com)

eograp c esources na ys s

and Support System) (www.baylor.edu)



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Raster or cellular data structures consist of- ,

scene.

boundaries or outlines of parcels on thesource document by listing the coordinates of

the boundaries or the coordinates of thevertical of polygons.



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Raster or cellular data structures:

The region of interest is subdivided into a uniform sizeand shape then encoded with a single category or

.

Raster structures offer ease of data storage and

manipulation, and therefore permit use of relativelys mp e compu er program.

Raster structures lend themselves to use withremotely sensed data because digital remote sensing

data are collected and presented in raster formats.Disadvantages are primary related to losses in

accurac and detail due to the codin of each cell.



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Vector or ol on data structures:

Provide more efficient of use of computer storage, finerdetail, and more accurate representation of shapes andsizes.

Its main disadvantages are the higher costs of encodingdata and the greater complexity required for the computerprograms that must manipulate data.

Today most GIS are designed to use vector format,although most accommodate raster data, or may useraster structures for selected anal ses.

Raster-to-vector and vector-to-raster conversions permitmixing of two kinds of data , although unnecessary


.


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Raster data structure Polygon data structure



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A rule is necessar to consistentl select which

category represents the cell in the database.The predominant category rule selects the single category

.

The category that falls beneath the dot is entered as the

category for the cell (b).e proce ure uses a o ran om y pos one w n eac

cell (c).

Relative to the predominant category rule, these

procedures improve the accuracy of thedatabase as a whole, but the error at each cell



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Gersmehl and Na ton 1982 studied these two

kinds of errors:Inventory error is error for database as a whole in

.

Inventory error is important for users of the GIS who

are interested in a regional overview.Classification error is the error in reporting the

contents of each cell.

detailed information concerning specific sites.



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Use of the dot randoml ositioned within each

cell reduces inventory error; for large areas,inventory errors may be quite small.

small relative to the pattern to be studied.

Classification error increases with use of theran om o me o , even oug erandomized selection may reduced inventoryerror.

It is possible to interpolate data from one cellsize to another: smaller cell to larger cell or


.


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Interpolation from one cell size to another



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The National Spatial Data Infrastructure (NSDI) is

nten e to orm a ramewor or e c ent exc ange o

geographic data between different organizations andbetween different computing systems.

es a s es s an ar or geograp c a a an a p anfor establishing a National Digital Geospatial Data

Framework.developed as part of a broader effort to establishguidelines for information processing within the federal

. STDS is intended to minimize problems encountered in

transferring data between agencies by establishing



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Relationship between Remotely

ense an

There are several avenues for incor oratinremotely sensed data into the GIS.

Manual interpretation of aerial images or satelliteima es.

Digital remote sensing data are analyzed or classifiedusing automated methods to produce conventional

a er ma s and ima es, which are then di itized forentry into the GIS.

Digital remote sensing data are analyzed or classifiedusing automated method, then are retained in digital

ormat or entry nto t e , us ng re ormatt ng orgeometric corrections as required.Digital remote sensing data are entered directly in


e r raw orm as a a or e


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Contributions of GIS to Remote

ensing

spatial data within an accurate planimetricframework and shares some of the sametechniques and principals.

GIS is well positioned to support the practice ofremote sensing, such as:

Mission planning

nc ary ata

Collection, organization, and visualization of



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ensing

Example of a GIS to organize and display field data


for use in remote sensing analysis


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ensing

A portion of the OakMapper web page illustrating the use of


GIS to provide a system for remote sensing flights plan


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Contributions of Remote Sensing

to I

support GIS operation, such as:

layers for GIS

backdrop for GIS

of updating GIS



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to I

GIS can provide ancillary data to assist in image classification



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to I

Remotely sensed data can contribute thematic data to a GIS



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to I In 1988 National Center for Geo ra hic Information

and Analysis (NCGIA) is established to lead the effortto conduct fundamental research on the analysis of

applications of GIS.

Other related organization devoted to GIS include theUniversity Consortium for Geographic InformationScience and Federal Geographic Data Committee.

Mobile GIS

-



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Mangrove forest areac ange etect on

procedureHussin et. al., 1999



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Remotely sensed data can contribute thematic data to a GIS


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The concept of the GIS is very important inremote sensing and it is ideally permits full

utilization of the goals of remote sensing. , ,

compatibility, improvements in image geometrywill improve the usefulness of remotely sensed

. The remote sensing and GIS has mutually

supporting and so many close relationships to

eac ano er.



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James B. Cam bell. Introduction to Remote Sensin -fourth edition, The Guilford press, New York, 2007.

Prof. S. Ramachandran. Application of Remote Sensing, .

Yang Hong, et all. Use of satellite remote sensing data in

the mapping of global landslide susceptibility, Springer. . .

U.S. Department of Transportation and NASA.Commercial remote sensing technologies application to

ranspor a on Etc.


Ch.15 Application GIS

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