Post on 12-Jun-2015
transcript
1GTAQ 2012
Collection and Interpretation of Remote Sensing Data
Dr. Kasper Johansen, Email: k.johansen@uq.edu.auBiophysical Remote Sensing Group
School of Geography, Planning and Environmental Management The University of Queensland
600m|______| 140m|______| 70m|______|
2GTAQ 2012
Objectives of talk
To present how to collect and access remote sensing image data and introduce selected image interpretation approaches and study exercises for high school students
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Outline of talk
Introduction to remote sensing
Collection of remote sensing data
Interpretation of remote sensing data: Short study on linking field and image data Student exercise 1 Short study on image interpretation cues Student exercise 2
Summary of this talk
Questions and Resources
Demonstration of Remote Sensing Toolkit for learning purposes
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What is Remote Sensing and Why Use It
The science and art of obtaining information about an object, area or phenomenon through the analysis of data collected by a device that is not in contact with the object, area or phenomenon under investigation (Lillesand et al., 2004:1)
Not a Remote Sensing Measurement
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What is Remote Sensing and Why Use It
Rockhampton/Gladstone MODIS Image February 11, 2003 Source CSIRO
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What is Remote Sensing and Why Use It
The science and art of obtaining information about an object, area or phenomenon through the analysis of data collected by a device that is not in contact with the object, area or phenomenon under investigation (Lillesand et al., 2004:1)
Gladstone
Rockhampton
Remote Sensing Measurement
Susp. sediment concentration
Not a Remote Sensing Measurement
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What is Remote Sensing and Why Use It
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Applications: Cyclone Yasi
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Applications: Biomass mapping
Measured in field
Mea
sure
d by
Sat
ellit
e
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Application: Surface Temperature
http://earthobservatory.nasa.gov/IOTD/view.php?id=36699
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Application: Elevation mapping
Digital Elevation Model LIDAR
12ENVM3201 - April 2011
Application: Elevation mapping
LiDAR (Light Detection and Ranging): LiDAR pulses from airborne transmitter Height difference between surface features = Time difference for returns High positional accuracy Very suitable for deriving vegetation structural and geomorphic
information
13ENVM3201 - April 2011
Application: Elevation mapping
LiDAR data examples with high point density
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Application: Elevation mapping
Predicted for 5.4 m
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Application: Elevation mapping
Predicted floodingJan 2011
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Application: Elevation mapping
Actual floodingJan 2011
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Application: Coral reef mapping
GPS towed
by diverEvery dot is a photo on the reef
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Application: Coral reef mapping
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Remote Sensing Applications
Response to increasing application areas = increasing data dimensionality and availability
Need to carefully select data and balance spatial resolution, spectral resolution, temporal resolution, acquisition costs and processing costs
Always question where data comes from and how it was derived - metadata
Push towards public access to data sets and open source processing tools to increase data sharing
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Remote Sensing Applications
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Collection of Remote Sensing Data
How do you get access to remote sensing data and what are the costs? High spatial resolution imagery:
Geoimage, SKM, AAM, Fugro Spatial Solutions ~ $30/km2
Airborne optical and LiDAR data: AAM, Fugro, AEROmetrex, ARA, Hyvista ~ $1500/km2
Free Imagery: USGS EarthExplorer
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Collection of Remote Sensing Data
How do you get access to remote sensing data and what are the costs? High spatial resolution imagery:
Geoimage, SKM, AAM, Fugro Spatial Solutions ~ $30/km2 Airborne optical and LiDAR data:
AAM, Fugro, AEROmetrex, ARA, Hyvista ~ $1500/km2
Free Imagery: USGS EarthExplorer Google Earth – but not geo-referenced
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Collection of Remote Sensing Data
How do you get access to remote sensing data and what are the costs? High spatial resolution imagery:
Geoimage, SKM, AAM, Fugro Spatial Solutions ~ $30/km2 Airborne optical and LiDAR data:
AAM, Fugro, AEROmetrex, ARA, Hyvista ~ $1500/km2
Free Imagery: USGS EarthExplorer Google Earth – but not geo-referenced TERN Data Discovery Portal
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Interpretation of Remote Sensing Data at Different
Spatial Scales
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Outline of talk
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Outline of talk
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Outline of talk
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Mapping Condition of Savanna Riparian Zones in North Australia
Case Study 1
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1. Tropical Savanna Riparian Zones
Australian tropical savannas Riparian zones
Source: Tropical Savannas CRC, 2003
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1. Importance of Riparian Zones
Provision of stream shade
Prevention of erosion
Nutrient source from litter fall
Natural filtering of pollutants
Wildlife habitat
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2. Objective
To map biophysical parameters suitable for assessing the environmental condition of Australian savanna riparian zones at local to regional scales based on the integration of field survey and high spatial resolution image data.
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3. Study Area – Daly River
Map of part of the Northern Territory
Darwin
KatherineApproximate scale
2km I____________I
2.4m pixels
0.6m pixels
QuickBird image of the Daly River study area
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3. Study Area – Daly River
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4. Methods - Field Survey Data Field measurements of 5m
x 5m quadrats on both sides of transect line – 10m wide transects
Parameters:1. Riparian zone width2. River channel width3. Percentage Canopy Cover4. Leaf Area Index (LAI)5. Ground cover6. High impact weeds7. Tree species8. Bank stability9. Flood damage10. Vegetation overhang
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4. Methods - Field Survey Data
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4. Methods - Field Survey Data
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4. Methods - Field Survey Data
Approximate scale
300m I_______________I
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5. Results – Biophysical Models
Daly River 2005
y = 1.4419Ln(x) + 1.2253
R2 = 0.7893, n = 548
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
0 0.2 0.4 0.6 0.8 1SAVI
Per
cen
tag
e C
ano
py
Co
ver
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5. Results – Biophysical Maps
Pan-sharpened QuickBird Image
Percentage Canopy Cover Map
Approximate scale
100m I_________I
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4. Methods - Field Survey Data
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5. Results – Biophysical Models
Daly River 2005
y = 9.5382x - 4.4086
R2 = 0.7206, n = 548
0
1
2
3
4
5
6
0 0.2 0.4 0.6 0.8 1SAVI
Lea
f A
rea
Ind
ex
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5. Results – Biophysical Maps
Pan-sharpened QuickBird Image
Leaf Area index Map
Approximate scale
100m I_________I
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Processing sequence for object-based image classification
Original image
Segmented image
Classified image
Develop rule sets
4. Methods - Object-based classification
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5. Results – Image Classification
Approximate scale
500m I_________I
Multi-spectral QuickBird image 23 August 2004
Approximate scale
500m I_________I
Multi-spectral QuickBird image – 13 August 2005
Approximate scale
500m I_________I
Image Classification 2004
Approximate scale
500m I_________I
Image Classification 2005
Classification Accuracy - 2004
0102030405060708090
100
Clearedareas
Water Savanna Riparianzone
Transitionzone
Exposedbanks
Land Cover Classes
Pe
rce
nta
ge
Producer's Accuracy User's AccuracyTotal number of samples = 350
Classification Accuracy - 2005
0102030405060708090
100
Clearedareas
Water Savanna Riparianzone
Transitionzone
Exposedbanks
Land Cover Classes
Pe
rce
nta
ge
Producer's Accuracy User's AccuracyTotal number of samples = 350
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5. Results – Image Classification
River width of the Daly River - August 2005
0
10
20
30
40
50
60
70
80
90
100
0 1082 2129 3446 4511 6582 8068 9239 10434 11548 12559 13767 14917 17267 19073Distance (m)
Riv
er
wid
th (
m)
Average river width = 46.51m
Riparian zone width, west bank of the Daly River, 2005
0
20
40
60
80
100
120
140
0 962 1921 3270 5119 6548 8004 8907 9858 10888 11828 13723 15412 17247 18890Distance (m)
Rip
aria
n z
on
e w
idth
(m
)
Average riparian zone width, west bank = 53.57m
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7. Object 2 - Results
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5. Results – Bank Stability Map
Approximate scale
100m I_________I
Pan-sharpened QuickBird Image
Stream Bank Stability Map
Approximate scale
100m I_________I
Flood Damage Map
Approximate scale
100m I_________I
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6. Conclusions
Indicators of riparian zone condition that can be mapped with an accuracy feasible for multi-temporal assessment:
Percentage canopy cover Leaf area index Bank stability Flood damage Riparian zone width River width
Large sample size of field data to improve relationship between field and image based measurements
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Study Exercise 1: Considering Spatial Scale
Aim
To understand how environmental features (e.g. trees, buildings and landforms) are measured and represented in remotely sensed images.
Background
Any effective form of remote sensing requires in-depth experience and measurement of the environment you are working in. This suggested field exercise with provide this link which will enable a strong and realistic basis for image analysis and interpretation skills.
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Study Exercise 1: Considering Spatial Scale
Tasks Task 1: Position/Location using a GPS Task 2: What is in a pixel Task 3: Identifying features along a transect to match
up observations with image data Task 4: Comparing a high spatial resolution image
(e.g. Google Earth) with a Landsat image
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Study Exercise 1: Considering Spatial Scale
Task 1: Position/Location using a GPS Aim: To explain, demonstrate and measure
horizontal and vertical position using a global positioning system (GPS) receiver
Instrument: Hand held GPS receiver Basic Principles to Explain and Demonstrate:
Measurements of horizontal and vertical position, including map projections, coordinate systems, datum
GPS principles Measurement accuracy
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Study Exercise 1: Considering Spatial Scale
Task 1: Position/Location using a GPS Record the GPS position of a single location or
feature at 1 minute intervals for 5 minutes Use the GPS receiver to accurately map the
boundary of two features at the field site
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Study Exercise 1: Considering Spatial Scale
Task 1: Position/Location using a GPS
Position Measurement Note taker:
Name student:
Feature- Photo
file name:
Waypoint name
Easting Northing Height
EPE (estima
ted positio
nal error)
Mean position Standard deviation of position
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Study Exercise 1: Considering Spatial Scale
Task 1: Position/Location using a GPS
Feature Mapping Note taker:Feature 1 Waypoint
Feature- Photo file name:
Easting Northing Height EPE Distance between Points
1) 2) 3) 4) Feature 2Waypoint
Feature- Photo file name:
Easting Northing Height Distance between points
1)2)3)
i)
n)
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Study Exercise 1: Considering Spatial Scale
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Study Exercise 1: Considering Spatial Scale
http://www.earthpoint.us/ExcelToKml.aspx
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Study Exercise 1: Considering Spatial Scale
Task 2: What is in a pixel Aim: To measure and assess the effects of
increasing the pixel size of an imaging sensor Instrument: 2 x 50 m survey tapes, digital camara,
hand held GPS receiver, ranging poles Basic Principles to Explain and Demonstrate:
Principles of multi-spectral optical imaging systems – where do pixels come from
What controls the size of features detectable in an image Level of spatial detail required for mapping Common imaging sensor pixel and scene dimensions
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Study Exercise 1: Considering Spatial Scale
Task 2: What is in a pixel Use the two survey tapes to successively mark out
the boundaries of image pixels to be measured At each pixel size, take photos from the centre of
the pixel and record GPS corner coordinates For each pixel size record the number and
percentage coverage of different land cover types (soil, concrete, grass, trees, etc.)
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Study Exercise 1: Considering Spatial Scale
Task 2: What is in a pixel
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Study Exercise 1: Considering Spatial Scale
Note taker:Pixel size - no: photo name Waypoint name Easting Northing Height0.5 x 0.5 m 1 2 3 4 2.4 x 2.4m 1 5 6 7 10 x 10m 1 8 9 10 20 x 20 m 1 11 12 13 30 x 30 m 1 14 15 16 50 x 50 m 1 17 18 19
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Study Exercise 1: Considering Spatial Scale
Pixel composition: Note taker:Pixel size - Surface Cover Type % of pixel
coveredSketch (soil, concrete, grass, trees, asphalt,etc etc
0.5 x 0.5 m
2.4 x 2.4m
10 x 10m
20 x 20 m
30 x 30 m
50 x 50 m
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Study Exercise 1: Considering Spatial Scale
Task 3: Identifying features along a transect to match up observations with image data Aim: To measure and assess how land cover types
are represented in image data Instrument: 1 x 50 m survey tapes, digital camera,
hand held GPS receiver Basic Principles to Explain and Demonstrate:
What does the satellite see What does a pixel look like when multiple land cover types
occur within it Why is there a need for integrating field and image data
(calibration and validation of image maps)
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Study Exercise 1: Considering Spatial Scale
Task 3:Identifying features along a transect to match up observations with image data Locate a start point of the transect and lay out the
50 m tape Record the positions of the start and end points of
the transect using the GPS receiver Take photos along the transect Identify land cover types along the transect line and
make notes where along the transect these occur
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Study Exercise 1: Considering Spatial Scale
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Study Exercise 1: Considering Spatial Scale
Note taker:Distance along transect
no: photo name Land cover type
Easting Northing Height
0m - ? m 1 ? m - ? m 2
3 4 5 6 7 8 9 10 11 12
Display the location of the transect start and end points in Google Earth
Compare land cover observations with those identified in Google Earth and explain any observed differences
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Study Exercise 1: Considering Spatial Scale
Task 4: Comparing a high spatial resolution image with a Landsat image Aim: To compare two images with different spatial
resolutions Questions to Address:
When and why would you use the two different image types?
What are the pros and cons of using the two different image types?
Think of different applications suitable for using the two image types
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Study Exercise 1: Considering Spatial Scale
Landsat image Google Earth image
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Object-Based Mapping of Urban Areas
Case Study 2
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Urban Land Cover Mapping
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QuickBird image from 2005 500 0 500250 Meters
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Urban Land Cover Mapping
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500 0 500250 Meters
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Urban Land Cover Mapping
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500 0 500250 Meters
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Urban Land Cover Mapping
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500 0 500250 Meters
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Urban Land Cover Mapping
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500 0 500250 Meters
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Urban Land Cover Mapping
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500 0 500250 Meters
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Urban Land Cover Mapping
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500 0 500250 Meters
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Urban Land Cover Mapping
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500 0 500250 Meters
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Conclusions
Object-based image analysis can be used to map urban land cover classes at high spatial resolution
Shape and size of objects and context relationships were found very useful for mapping urban land cover classes
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Study Exercise 2: Interpreting images
Aim
To build an understanding and experience in the necessary skills for interpreting image data
Background
Manual interpretation of aerial photos and high spatial resolution image data is a well established science. This science has recently provided the basis for automated mapping approaches using object-based image analysis
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Study Exercise 2: Interpreting images
Image Interpretation Cues Tone/Colour: bright / actual colour Texture: frequency of change and arrangement of
tones Size: physical size of objects Shape: shape created by the boundaries of features Shadows: presence and extent Pattern: repetition of shape and tonal features Context (site and association): geographic location
constraints of features (e.g. beaches near water), positional association (e.g. aircraft, runway, airport)
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Study Exercise 2: Interpreting images
Image Interpretation Cues Terminology Tone/Colour: bright - dark / actual colour Texture: smooth - rough Size: physical size and dimensions of objects Shape: rectangular, circular, square, oval, etc. Shadows: presence and extent Pattern: regular - irregular Context (site and association): geographic location
constraints of features (e.g. beaches near water), positional association (e.g. aircraft, runway, airport)
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Study Exercise 2: Interpreting images
Questions: Identify image interpretation cues for the following
land cover types: mangroves, canal estate, sugar cane fields
Identity which interpretation cues are unique for certain land cover classes, which will allow recognition and discrimination and different land cover classes
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Study Exercise 2: Interpreting images
Interpretation
CueLand-
cover/use #1Land-
cover/use #2Land-
cover/use #3Tone – Colour
Texture
Size
Shape
Pattern
Shadow
Context
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Study Exercise 2: Interpreting images
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Study Exercise 2: Interpreting images
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Study Exercise 2: Interpreting images
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Summary of this talk Brief introduction about Remote Sensing
Case study on relating field and image data
Study exercise suitable for field trip
Case study on automated use of image interpretation cues
Study exercise suitable for the classroom
Further learning tools and resources
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www.gpem.uq.edu.au/cser-rstoolkit