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?)