module 3 introduction to gis - charles sturt university · introduction to gis ... platforms:...
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Module 3Introduction to GISLecture 8 – GIS data acquisition
GIS workflow
Data acquisition (geospatial data input)
•GPS
•Remote sensing (satellites, UAV’s)
•LiDAR
•Digitized maps
Attribute Data Management
•Data verification
•Database management
Exploratory Analysis
•Attribute and spatial data queries
•Geovisualization
Data Analysis
•Vector and raster data analysis
•Terrain mapping
•Spatial interpolation
•Network analysis
Geovisualization
(maps)
Chang, 2014, p.8-9
GIS data acquisition
http://www.interworldna.com/images/gtco/gtco-acculit.jpg
GPS
Digitisedmaps
Remote sensing
LiDAR
Aerial photography
Unmanned aerial vehicles
GIS data acquisition - vector
http://www.ahds.ac.uk/history/images/hpewmap.gif
GPS
Digitised maps
GIS data acquisition - raster
http://www.crisp.nus.edu.sg/~research/tutorial/context4.gif
http://oceanservice.noaa.gov/facts/lidar.jpg
Aerial photography
Remote sensing(Satellite images)
Remote sensing
Remote sensing definitions
REMOTE – sensor located at a platform, at some distance from the object or area (no physical contact)
Platforms: satellites, airplanes, unmanned aerial vehicles – drones
Sensors: placed in cameras with different characteristics
Why this matters for spatial data acquisition?
Fieldwork is costly and some places are not easilyaccessible
The person and equipment collecting the data mightinterfere with the objet or area being studied
Acquisition over larger areas, for several time periods
http://gpsworld.com/wp-content/uploads/2016/01/GIOVE-A_on_ground_node_full_image_2-300x300.jpg
Remote sensing definitions
SENSING – sensor capturing light reflected from the object or area (surface)
Why this matters for spatial data acquisition?
Surfaces reflect light differently according to their characteristics – that allows to differentiate them
Energy source (sun) required to illuminate a target.
Passive sensor – used the sun as source
Active sensor – own source of energy
When receiving light: surfaces reflect, absorb and transmit that light - electromagnetic (EM) energy (or radiation).The EM energy reflected by the surface is detected and recorded by the sensor and converted to digital images (each pixel in the image contains specific data related to the way the surface reflected light).
Remote sensing definitions – EM spectrumVisible light: 0.4 µm (blue) to 0.7 µm (red)
Wavelengths measured in microns or micrometres (µm) 10-6m.
Remote sensing definitions – EM spectrum / Specific data
SURFACE
https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcTXP2hKAq86TuCYlhU8_TK0HhluSOt-AAkG1LVmDFWg2k9_VvepPA https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcSr1z7PlBlKECFpUt-kowRXoHG96WN-buxsDxxRNNzY30FXxzjp
SENSOR
Sensors are EM spectrum selectiveSurfaces are EM spectrum selective
Each surface has a spectral signature(Allows differentiate trees, buildings, water…)
Each sensor has different bands
Digital/satellite images – spatial data
Specific data: each pixel records the amount of energy emitted for each band, converted afterwards into thematic data
Remote sensing definitions – Spectral signatures
http://www.markelowitz.com/Vegetation-Spectrum.png
Remote sensing images
Digital images – spatial data acquisition using remote sensing systems
Remote sensing images
Features to consider for remote sensing images:
• SPATIAL RESOLUTION (area extent and pixel size)(platform & sensor)• SPECTRAL RESOLUTION (number of bands)(sensor)• TEMPORAL RESOLUTION (acquisition frequency over the
same location)(platform & sensor)
Choosing the right image depends on the application but it is often a trade-off of these resolution features and the budget available.
https://upload.wikimedia.org/wikipedia/commons/3/35/Gujarat_Satellite_Imagery_2012.jpg https://encrypted-tbn3.gstatic.com/images?q=tbn:ANd9GcTMqQZSXukFQY1_FNghkuSDd2-otuW-mrKCsiWYmWWfQOjkpxs6NA
Remote sensing images – Spatial Resolution
http://content.satimagingcorp.com.s3.amazonaws.com/media/cms_page_media/120/spatial-resolution.jpg
• Spatial resolution: pixel size• Usually expressed in meters (for example,
a 30 m resolution means that two objects, thirty meters long or wide, sitting side by side, can be resolved on the satellite image)
• High resolution: pixel size from 0.4 to 4 m• Medium resolution• Low resolution: pixel size from 30 m to
more than 1 km
Remote sensing images – Spatial Resolution
30 m 10 m 2 m
Remote sensing images – Spatial Resolution
http://coast.noaa.gov/digitalcoast/_/img/3_image_spatial_resolution.png
1 m 10 m 30 m
Remote sensing images – Spectral resolution
Spectral resolution: number of spectral bands + regions of the EM spectrum
In terms of the spectral regions used, the remote sensing systems can be classified into:
Optical (include visible, near infrared, and shortwave infrared systems)
Thermal
Synthetic aperture radar (SAR)
In terms of the number of bands recorded, optical/thermal systems can be classified into:
Monospectral or panchromatic (single wavelength band)
Multispectral (tens of spectral bands)
Hyperspectral (hundreds of spectral bands)
Remote sensing images – Spectral resolution /Panchromatic
1m IKONOS panchromatic image of Bogota, ColombiaImage credit LAND INFO Worldwide Mapping, LLC, includes material Copyright © DigitalGlobe - Longmont, Colorado
10 m SPOT HRV panchromatic image of San Francisco, USA(http://fas.org/irp/imint/docs/rst/Sect13/Sect13_4b.html)
Remote sensing images – Spectral resolution /Multispectral
4 m IKONOS multispectral image of Cape Town, South AfricaI
Planetary Visions Limited1.5 m SPOT 6/7 multispectral image of the Kiribati Republic
Copyright: Airbus DS 2015
Remote sensing images – Spectral resolution /Hyperspectral
5m HyMap hyperspectral imagery from the Willouran ranges in South Australia.Carbonate materials are shown in blues and purples while siliclastic materials appear as oranges and reds.
Vegetation is displayed as green(J.L. Keeling and A.J. Mauger, 2000: "Application of Airborne Hyperspectral (HYMAP) Data to Map Variation in Carbonate Facies in Proterozoic Skillogalee Dolomite, Willouran Ranges, South Australia." 10th Australasian Remote Sensing
and Photogrammetry Conference, Adelaide, 2000; http://crcleme.org.au/NewsEvents/News/Archive/2003/hyperspecdataAM.html )
Remote sensing images – Temporal resolution
Revisiting frequency of a satellite sensor for a specific location
High temporal resolution: scans the same location less than 24 hours to 3 days after the first scan.
Medium temporal resolution refers to 4 to 16 days.
Low temporal resolution refers to 16 days.
Examples of satellite images
The WorldView-3 Satellite Sensor provides 31 cm panchromatic resolution (450 - 800 nm), 1.24 m multispectral resolution (400 - 1040 nm) and 3.7 m short wave infrared resolution (1195 - 2365 nm). It has an average revisit time of less than 1 day.
Remote sensing images
Visualization – how do we choose to display spectral data for visual analysis
Image visualization
• Image visualization: assigning the right "colours" to the image bands (colour composite image) to improve image interpretation
• Colour composite image: associating each band (not necessarily the visible band) to a separate primary colour (red, green and blue - RGB)
http://www.nasa.gov/vision/earth/lookingatearth/socal_wildfires_oct07.html
TRUE COLOUR FALSE COLOUR
Image visualization – False colour composite
Common false colour band combination for image interpretation (identify healthy vegetation):Near infrared, green, and red bands displayed as red, blue and green
• Vegetation appears in different shades of red depending on the types and conditions of the vegetation, since it has a high reflectance in the NIR band.
TRUE COLOUR FALSE COLOUR
Remote sensing visualization
Remote sensing software (image viewing): red, green and blue colours assigned to any of the bands collected by the sensor.
Satellite-derived remotely sensed images are often viewed as false colour composites: red band to show near-infrared, green band to show visible red and blue band to show visible green.
False colour composite image (Landsat Thematic Mapper image of Morro Bay, California)
LiDAR
LiDAR - Light Detection and Ranging
(3D) laser scanning method used to map the surface of the Earth.
Often mounted in an aircraft or any other flying device (although it is also possible to have ground mounted devices), the LiDAR device uses light in the form of a pulsed laser to measure ranges (variable distances) to the Earth.
The light pulses combined with GPS data (altitude, latitude and longitude) recorded by LiDAR system generate precise, 3D data for the shape of the Earth and its surface characteristics.
The data produced is in a 'point cloud' format: 3D array of points, each having x, y and z positions relative to a chosen coordinate system.
A single LiDAR survey can easily generate billions of points totalling several terabytes.
LiDAR - Light Detection and Ranging
http://soundwaves.usgs.gov/2003/01/Lidardiagram.jpg
http://oceanservice.noaa.gov/facts/lidar.jpg
Cool LiDAR applications
https://www.youtube.com/watch?v=o6Kq4XF1zKU
LiDAR helps revealing hidden cities
LiDAR helps creating exciting music videos
http://www.youtube.com/watch?v=8nTFjVm9sTQ
https://www.youtube.com/watch?v=0XdqGNu9bhk
LiDAR helps natural park management & cultural heritage
Unmanned aerial vehicles (UAVs)
Remotely controlled aircraft systems able to fly at low altitudes, carrying different sensors from a camera to a LiDAR
If the image resolution is not high enough to see exact areas of devastation or change, coverage of an entire affected area is not available, or imagery is simply too expensive to acquire, then an analysis will be difficult to complete. The generally low-cost high resolution image capture capability of UAV’s creates the potential for them to fill the data gap between satellites, ortophotos and ground surveying.
By attaching different sensors, it is possible to gather a wide range of environmental data (air pollution, land cover surveying, wildlife recording, heat mapping).
They can also assist with emergency management operations, eliminating the human-risk factor from the operation while also sending back real-time information.
UAV’s in action
Emergency management
https://www.youtube.com/watch?v=AOXmju2bCpQ
Conservation management:
https://youtu.be/LT9q6kra9Oc
More about GIS analysis next week
SCI103 notes:Start planning Assessment 5(any questions yet?)