Collection and Interpretation of Remote Sensing Data, Kasper Johansen, University of Queensland

1GTAQ 2012

Collection and Interpretation of Remote Sensing Data

Dr. Kasper Johansen, Email: [email protected] 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

3GTAQ 2012

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

4GTAQ 2012

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

5GTAQ 2012


Rockhampton/Gladstone MODIS Image February 11, 2003 Source CSIRO

6GTAQ 2012


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

7GTAQ 2012


8GTAQ 2012

Applications: Cyclone Yasi

9GTAQ 2012

Applications: Biomass mapping

Measured in field

Mea

sure

d by

Sat

ellit

e

10GTAQ 2012

Application: Surface Temperature

http://earthobservatory.nasa.gov/IOTD/view.php?id=36699

11GTAQ 2012

Application: Elevation mapping

Digital Elevation Model LIDAR

12ENVM3201 - April 2011


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


LiDAR data examples with high point density

14GTAQ 2012


Predicted for 5.4 m

15GTAQ 2012


Predicted floodingJan 2011

16GTAQ 2012


Actual floodingJan 2011

17GTAQ 2012

Application: Coral reef mapping

GPS towed

by diverEvery dot is a photo on the reef

19GTAQ 2012

Application: Coral reef mapping

20GTAQ 2012

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

21GTAQ 2012

Remote Sensing Applications

22GTAQ 2012

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

23GTAQ 2012

24GTAQ 2012



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

25GTAQ 2012

26GTAQ 2012



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

27GTAQ 2012

28GTAQ 2012

29GTAQ 2012

Interpretation of Remote Sensing Data at Different

Spatial Scales

30GTAQ 2012

Outline of talk

31GTAQ 2012

Outline of talk

32GTAQ 2012

Outline of talk

33GTAQ 2012

34GTAQ 2012

35GTAQ 2012

36GTAQ 2012

Mapping Condition of Savanna Riparian Zones in North Australia

Case Study 1

37GTAQ 2012

1. Tropical Savanna Riparian Zones

Australian tropical savannas Riparian zones

Source: Tropical Savannas CRC, 2003

38GTAQ 2012

1. Importance of Riparian Zones

Provision of stream shade

Prevention of erosion

Nutrient source from litter fall

Natural filtering of pollutants

Wildlife habitat

39GTAQ 2012

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.

40GTAQ 2012

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

41GTAQ 2012

3. Study Area – Daly River

42GTAQ 2012

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

43GTAQ 2012

4. Methods - Field Survey Data

44GTAQ 2012


45GTAQ 2012


Approximate scale

300m I_______________I

46GTAQ 2012

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

47GTAQ 2012

5. Results – Biophysical Maps

Pan-sharpened QuickBird Image

Percentage Canopy Cover Map

Approximate scale

100m I_________I

48GTAQ 2012


49GTAQ 2012

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

50GTAQ 2012

5. Results – Biophysical Maps


Leaf Area index Map

Approximate scale

100m I_________I

51GTAQ 2012

Processing sequence for object-based image classification

Original image

Segmented image

Classified image

Develop rule sets

4. Methods - Object-based classification

52GTAQ 2012

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

53GTAQ 2012

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

54GTAQ 2012

7. Object 2 - Results

55GTAQ 2012

5. Results – Bank Stability Map

Approximate scale

100m I_________I


Stream Bank Stability Map

Approximate scale

100m I_________I

Flood Damage Map

Approximate scale

100m I_________I

56GTAQ 2012

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

57GTAQ 2012

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.

58GTAQ 2012


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

59GTAQ 2012


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

60GTAQ 2012


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

61GTAQ 2012


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

62GTAQ 2012


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)

63GTAQ 2012


64GTAQ 2012


http://www.earthpoint.us/ExcelToKml.aspx

65GTAQ 2012


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

66GTAQ 2012


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.)

67GTAQ 2012


Task 2: What is in a pixel

68GTAQ 2012


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

69GTAQ 2012


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

70GTAQ 2012


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)

71GTAQ 2012


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

72GTAQ 2012


73GTAQ 2012


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

74GTAQ 2012


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

75GTAQ 2012


Landsat image Google Earth image

76GTAQ 2012

Object-Based Mapping of Urban Areas

Case Study 2

77

Urban Land Cover Mapping

GTAQ 2012

QuickBird image from 2005 500 0 500250 Meters

78


GTAQ 2012

500 0 500250 Meters

79


GTAQ 2012

500 0 500250 Meters

80


GTAQ 2012

500 0 500250 Meters

81


GTAQ 2012

500 0 500250 Meters

82


GTAQ 2012

500 0 500250 Meters

83


GTAQ 2012

500 0 500250 Meters

84


GTAQ 2012

500 0 500250 Meters

85

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

GTAQ 2012

86GTAQ 2012

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

87GTAQ 2012


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)

88GTAQ 2012


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)

89GTAQ 2012


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

90GTAQ 2012


Interpretation

CueLand-

cover/use #1Land-

cover/use #2Land-

cover/use #3Tone – Colour

Texture

Size

Shape

Pattern

Shadow

Context

91GTAQ 2012


92GTAQ 2012


93GTAQ 2012


94GTAQ 2012

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

95GTAQ 2012

www.gpem.uq.edu.au/cser-rstoolkit

Collection and Interpretation of Remote Sensing Data, Kasper Johansen, University of Queensland

Technology

remote sensing dataand

remote sensing measurementgtaq

talk gtaq

data sets

data sharinggtaq

coral reef mapping gtaq

cyclone yasi gtaq

increasing data dimensionality