Ch13-RS Urban Landscape - gers.uprm.edu · Urban Landscape REFERENCE: Remote Sensing of the Environment John R. Jensen (2007) Second Edition Pearson Prentice Hall Urban Remote Sensing

11

CHAPTER CHAPTER 1313CHAPTER CHAPTER 13:13:Remote Sensing ofRemote Sensing ofUrban LandscapeUrban Landscape

REFERENCE: Remote Sensing REFERENCE: Remote Sensing of the Environment of the Environment John R. Jensen (2007)John R. Jensen (2007)Second EditionSecond EditionPearson Prentice HallPearson Prentice Hall

pp

Urban Remote Sensing Users

• Zoning regulation• Commerce and economic development• Tax assessor• Transportation and utilities• Parks, recreation, and tourism

E• Emergency management• Real Estate• Developers

22

Urban/Suburban Temporal Resolution Considerations

P i l l l i• Partial or complete clearing

• Land subdivision

• Roads

• Buildings

• Landscaping

Stages of Development

33

1974

1,040 urbanhectareshectares

1994

3,263 urbanhectares

315% increase

AERIAL PHOTOGRAPHY TODETERMINE TEMPORAL CHANGES

La Parguera gin 1936

La Parguera in the 80's

44

Remote SensingResolution

Requirements

Urban Remote Sensing

• Minimum spatial resolution of 0.25 – 5 m

• Minimum of four pixels within an object to identify(one-half the width of the smallest dimension -i.e. 5 m mobile homes requires at least 2.5 m data)

R l f h i i i h d• Role of shape, size, texture, orientation, pattern, shadow

• Land use vs. land cover?

55

Temporal and Spatial Characteristics of Urban Attributes and Remote Sensing Systems

• Temporal and spatial resolution requirements necessary to extract socio-economic and some biophysical information for selected urban/suburban attributes are presented.

• The goal is to relate the information requirements with the current and proposed remote sensing systems to determine if there are substantive gaps in capability.

• We need improved algorithms and methods for extracting urban/suburban information from remote sensor data.

Temporal and Spatial Characteristics of Urban Attributes and Remote Sensing Systems

Observations:

• There are a number of remote sensing systems that currently provide some of the desired urban/socio-economic information when the spatial resolution required is > 5 x 5 m and the temporal resolution is between 1 and 55 days.

• As demonstrated, very high spatial resolution data (<1 x 1 m) is required to satisfy many of the socio-economic data requirements. This is especially true for urban areas in developing countries.

66

Digital Frame Camera Imagery of Harbor Town, Hilton Head, SC

1 x 1 ft spatial

resolution

Panchromatic 3 x 3-in Image of Popular Bluff, MO Obtained on February 15, 2000 at 5,000 ft AGL Using A Digital

Array Panoramic Camera with 32,000 x 8,000 Detectors

Courtesy of Image America, Inc.

Swath width1.5 mi

77

IKONOS Panchromatic Stereopair of Columbia, SC Airport

November 15, 2000

IKONOS Panchromatic

Panchromatic Sharpened Near-infrared

Columbia, SC on October 15, 2000

88

IKONOS Panchromatic Sharpened Near-infrared Image Overlayed on a USGS Digital Elevation Model

Columbia, SC October 15,

2000

Clear polygons represent the spatial and temporal

characteristicsof selected urban attributes

nute

s

Gray boxes depict the spatial and temporal

Tem

pora

l R

esolu

tion i

n m

in

characteristics of the remote sensing systems

that may be used to extract the

required urban information

99

Urban Minimum Resolution Requirements

Land Use/Cover Temporal Spatial Spectral

USGS Level 1 5-10 yrs 20-100 m VIS-NIR

USGS Level 2 5-10 yrs 5-20 m VIS-NIR

USGS Level 3 3-5 yrs 1-5 m Pan-VIS-NIR

USGS Level 4 1-3 yrs 0.25-1 m Pany

Classification Levels

1 Urban or Built-up

11 Residential111 Single-Family Residential

1111 House, houseboat, hut, tent1112 Mobile home

112 Multiple-Family Residential1121 Duplex1122 Triplex1123 Apartment Complex or Condominium1123 Apartment Complex or Condominium1124 Mobile home (trailer) park

1010

58

105

2

3

58

106

1 year

23

5

8107

5 year

10 year

44 day 55 day

JERS-1 MSS 18 x 24 Radar 18 x 18

IRS-1 AB LISS-1 72.5 x 72.5

LISS-2 36.25 x 36.25 IRS-1C

Pan 5.8 x 5.8 LISS 3 23 x 23; MIR 70 x 70

2 year

100,000 min

3 year4 year

30 d

180 day

B2

C1

U1

15 yearS2

DE1, E2

C2

E1

LIVB1T2U2

U3

D1 D2S3 S1

T1

LILII

SPOT HRV 1,2,3 and 4 (1998) Pan 10 x10

MSS 20 x 20 SPOT HRG (2002)

Pan 3 x 3; 5 x 5 (not shown) MSS 10 x 10; MIR 20 x 20

LIII

1 m0.3 5 100 m10 20

58

105

2

3

58

106

1 year

23

5

8107

5 year

10 year

44 day 55 day


IRS-1 AB LISS-1 72.5 x 72.5

LISS-2 36.25 x 36.25 IRS-1C

Pan 5.8 x 5.8 LISS 3 23 x 23; MIR 70 x 70

2 year

100,000 min

3 year4 year

30 d

180 day

B2

C1

U1

15 yearS2

DE1, E2

C2

E1

LIVB1T2U2

U3

D1 D2S3 S1

T1

LILII

SPOT HRV 1,2,3 and 4 (1998) Pan 10 x10



LIII

1 m0.3 5 100 m10 20

Land Use /Land Cover

30

50

80

20

30

50

80

20

n

102

103

104

5

2

3

5

2

3

5

2

3

5

9 day

1 day

1 hr

12 hr

8

8

8

3 day

2 day

4 day

26 day

NOAA AVHRR LAC 1.1 x 1.1 km

GAC 4 x 4 km

RADARSAT 11 x 9

100 x 100

MODIS* Land 0.25 x 0.25 km Land 0.50 x 0.50 km

Ocean 1 x 1 km Atmo 1 x 1 km TIR 1 x 1 km

EOSAT/Space Imaging IKONOS (1998)

Pan 1 x 1 MSS 4 x 4

IRS-P5 (1999) Pan 2.5 x 2.5

ORBIMAGE

OrbView 3 (1999) Pan 1 x 1 MSS 4 x 4

22 day 16 day

LISS-3 23 x 23; MIR 70 x 70

1,000 min

10,000 min

100 min

5 day

30 day

LANDSAT 4,5 MSS 79 x 79 TM 30 x 30

LANDSAT 7 (1998) Pan 15 x 15

EarthWatch Earlybird (1998)

Pan 3 x 3 MSS 15 x 15

Quickbird (1998) 0.82 x 0.82 3.28 x 3.28

M1GOES

VIS 0.9 X 0.9 km TIR 8.0 X 8.0 km

DE2

DE3 DE4 DE5

M5

102

103

104

5

2

3

5

2

3

5

2

3

5

9 day

1 day

1 hr

12 hr

8

8

8

3 day

2 day

4 day

26 day


GAC 4 x 4 km

RADARSAT 11 x 9

100 x 100




Pan 1 x 1 MSS 4 x 4

IRS-P5 (1999) Pan 2.5 x 2.5

ORBIMAGE


22 day 16 day

LISS-3 23 x 23; MIR 70 x 70

1,000 min

10,000 min

100 min

5 day

30 day


LANDSAT 7 (1998) Pan 15 x 15


Pan 3 x 3 MSS 15 x 15

Quickbird (1998) 0.82 x 0.82 3.28 x 3.28

M1GOES

VIS 0.9 X 0.9 km TIR 8.0 X 8.0 km

DE2

DE3 DE4 DE5

M5

Relationship between sensor system spatial

resolution and land use/land cover class

0.3

0.5

1

3

10

5

I IIIII IV

4

2

0.3

0.5

1

3

10

5

I IIIII IV

4

2A

ppro

xim

ate

IFO

V (

m)

Tem

pora

l Res

olut

ion

0.2 1.0 2 3 5 10 2 3 5 102 103 1042 3 5 2 3 5

10

2

3

5

8

23

5

30 min

10 min

1 m 10 30 100 m 1 km

1000 m 5 km 10 km

.8.5

5 min

3 min

532 15 20

4 8

Ground Doppler Radar

4 x 4 km

METEOSAT VISIR 2.5 x 2.5 km

TIR 5 x 5 km

Aerial Photography 0.3 x 0.3 m (0.98 x 0.98 ft.) 1 x 1 m (3.281 x 3.281 ft.)

T3

T4

M3

M4

0.3

M2

0.2 1.0 2 3 5 10 2 3 5 102 103 1042 3 5 2 3 5

10

2

3

5

8

23

5

30 min

10 min

1 m 10 30 100 m 1 km

1000 m 5 km 10 km

.8.5

5 min

3 min

532 15 20

4 8


4 x 4 km


TIR 5 x 5 km


T3

T4

M3

M4

0.3

M2

Land Cover Class (level)I IIIII IV

Land Cover Class (level)I IIIII IV

Spatial Resolution in meters

Temporal Spatial Resolution Resolution

L1 - USGS Level I 5 - 10 years 20 - 100 mL2 - USGS Level II 5 - 10 years 5 - 15 m L3 - USGS Level III 3 - 5 years 1 - 5 mL4 - USGS Level IV 1 - 3 years 0.3 - 1 m

8105

2

3

58

106

1 year

23

5

8107

5 year

10 year

44 day55 day


IRS-1 AB LISS-1 72.5 x 72.5

LISS-2 36.25 x 36.25 IRS-1C

Pan 5 8 x 5 8

2 year

100,000 min

3 year4 year

180 day

B2

C1

U1

15 yearS2

DE1, E2

C2

E1

LIVB1T2U2

U3

D1 D2S3 S1

T1

LILII

SPOT HRV 1,2,3 and 4 (1998) Pan 10 x10



LIII

1 m0.3 5 100 m10 20

8105

2

3

58

106

1 year

23

5

8107

5 year

10 year

44 day55 day


IRS-1 AB LISS-1 72.5 x 72.5

LISS-2 36.25 x 36.25 IRS-1C

Pan 5 8 x 5 8

2 year

100,000 min

3 year4 year

180 day

B2

C1

U1

15 yearS2

DE1, E2

C2

E1

LIVB1T2U2

U3

D1 D2S3 S1

T1

LILII

SPOT HRV 1,2,3 and 4 (1998) Pan 10 x10



LIII

1 m0.3 5 100 m10 20Building and Cadastral (Property Line) Infrastructure

102

103

104

5

2

3

5

2

3

5

2

3

5

9 day

1 day

1 hr

12 hr

8

8

8

3 day

2 day

4 day

26 day

44 day


GAC 4 x 4 km

RADARSAT 11 x 9

100 x 100




Pan 1 x 1 MSS 4 x 4

IRS-P5 (1999) Pan 2.5 x 2.5

ORBIMAGE


22 day 16 day

Pan 5.8 x 5.8 LISS-3 23 x 23; MIR 70 x 70

1,000 min

10,000 min

100 min

5 day

30 day


LANDSAT 7 (1998) Pan 15 x 15


Pan 3 x 3 MSS 15 x 15

Quickbird (1998) 0.82 x 0.82 3.28 x 3.28

M1GOES

VIS 0.9 X 0.9 km TIR 8.0 X 8.0 km

DE2

DE3 DE4 DE5

M5

102

103

104

5

2

3

5

2

3

5

2

3

5

9 day

1 day

1 hr

12 hr

8

8

8

3 day

2 day

4 day

26 day

44 day


GAC 4 x 4 km

RADARSAT 11 x 9

100 x 100




Pan 1 x 1 MSS 4 x 4

IRS-P5 (1999) Pan 2.5 x 2.5

ORBIMAGE


22 day 16 day

Pan 5.8 x 5.8 LISS-3 23 x 23; MIR 70 x 70

1,000 min

10,000 min

100 min

5 day

30 day


LANDSAT 7 (1998) Pan 15 x 15


Pan 3 x 3 MSS 15 x 15

Quickbird (1998) 0.82 x 0.82 3.28 x 3.28

M1GOES

VIS 0.9 X 0.9 km TIR 8.0 X 8.0 km

DE2

DE3 DE4 DE5

M5

Tem

pora

l Res

olut

ion

Derived from 0.3 x 0.3 m (1 x 1 ft.) spatial resolution

0.2 1.0 2 3 5 10 2 3 5 102 103 1042 3 5 2 3 5

10

2

3

5

8

23

5

30 min

10 min

1 m 10 30 100 m 1 km

1000 m 5 km 10 km

.8.5

5 min

3 min

532 15 20

4 8


4 x 4 km


TIR 5 x 5 km


T3

T4

M3

M4

0.3

M2

0.2 1.0 2 3 5 10 2 3 5 102 103 1042 3 5 2 3 5

10

2

3

5

8

23

5

30 min

10 min

1 m 10 30 100 m 1 km

1000 m 5 km 10 km

.8.5

5 min

3 min

532 15 20

4 8


4 x 4 km


TIR 5 x 5 km


T3

T4

M3

M4

0.3

M2


stereoscopic, panchromatic aerial photography


B1 - building perimeter, area, volume, height 1 - 2 years 0.3 - 0.5 mB2 - cadastral mapping (property lines) 1 - 6 mo 0.3 - 0.5 m

1111

8105

2

3

58

106

1 year

23

5

8107

5 year

10 year

44 day55 day


IRS-1 AB LISS-1 72.5 x 72.5

LISS-2 36.25 x 36.25 IRS-1C

Pan 5 8 x 5 8

2 year

100,000 min

3 year4 year

180 day

B2

C1

U1

15 yearS2

DE1, E2

C2

E1

LIVB1T2U2

U3

D1 D2S3 S1

T1

LILII

SPOT HRV 1,2,3 and 4 (1998) Pan 10 x10



LIII

1 m0.3 5 100 m10 20

8105

2

3

58

106

1 year

23

5

8107

5 year

10 year

44 day55 day


IRS-1 AB LISS-1 72.5 x 72.5

LISS-2 36.25 x 36.25 IRS-1C

Pan 5 8 x 5 8

2 year

100,000 min

3 year4 year

180 day

B2

C1

U1

15 yearS2

DE1, E2

C2

E1

LIVB1T2U2

U3

D1 D2S3 S1

T1

LILII

SPOT HRV 1,2,3 and 4 (1998) Pan 10 x10



LIII

1 m0.3 5 100 m10 20Transportation Infrastructure

102

103

104

5

2

3

5

2

3

5

2

3

5

9 day

1 day

1 hr

12 hr

8

8

8

3 day

2 day

4 day

26 day

44 day


GAC 4 x 4 km

RADARSAT 11 x 9

100 x 100




Pan 1 x 1 MSS 4 x 4

IRS-P5 (1999) Pan 2.5 x 2.5

ORBIMAGE


22 day 16 day

Pan 5.8 x 5.8 LISS-3 23 x 23; MIR 70 x 70

1,000 min

10,000 min

100 min

5 day

30 day


LANDSAT 7 (1998) Pan 15 x 15


Pan 3 x 3 MSS 15 x 15

Quickbird (1998) 0.82 x 0.82 3.28 x 3.28

M1GOES

VIS 0.9 X 0.9 km TIR 8.0 X 8.0 km

DE2

DE3 DE4 DE5

M5

102

103

104

5

2

3

5

2

3

5

2

3

5

9 day

1 day

1 hr

12 hr

8

8

8

3 day

2 day

4 day

26 day

44 day


GAC 4 x 4 km

RADARSAT 11 x 9

100 x 100




Pan 1 x 1 MSS 4 x 4

IRS-P5 (1999) Pan 2.5 x 2.5

ORBIMAGE


22 day 16 day

Pan 5.8 x 5.8 LISS-3 23 x 23; MIR 70 x 70

1,000 min

10,000 min

100 min

5 day

30 day


LANDSAT 7 (1998) Pan 15 x 15


Pan 3 x 3 MSS 15 x 15

Quickbird (1998) 0.82 x 0.82 3.28 x 3.28

M1GOES

VIS 0.9 X 0.9 km TIR 8.0 X 8.0 km

DE2

DE3 DE4 DE5

M5

Irmo, S.C. TIGER road network updated using SPOT 10 x 10 m data

Tem

pora

l Res

olut

ion

0.2 1.0 2 3 5 10 2 3 5 102 103 1042 3 5 2 3 5

10

2

3

5

8

23

5

30 min

10 min

1 m 10 30 100 m 1 km

1000 m 5 km 10 km

.8.5

5 min

3 min

532 15 20

4 8


4 x 4 km


TIR 5 x 5 km


T3

T4

M3

M4

0.3

M2

0.2 1.0 2 3 5 10 2 3 5 102 103 1042 3 5 2 3 5

10

2

3

5

8

23

5

30 min

10 min

1 m 10 30 100 m 1 km

1000 m 5 km 10 km

.8.5

5 min

3 min

532 15 20

4 8


4 x 4 km


TIR 5 x 5 km


T3

T4

M3

M4

0.3

M2


Bridge assessment using high resolution oblique

photography

Parking/traffic studies require high

spatial/temporal resolution


T1 - general road centerline 1 - 5 years 1 - 10 mT2 - precise road width 1 - 2 years 0.3 - 0.5 m T3 - traffic count studies (cars, planes etc.) 5 - 10 min 0.3 - 0.5 mT4 - parking studies 10 - 60 min 0.3 - 0.5 m

8105

2

3

58

106

1 year

23

5

8107

5 year

10 year

44 day55 day


IRS-1 AB LISS-1 72.5 x 72.5

LISS-2 36.25 x 36.25 IRS-1C

Pan 5 8 x 5 8

2 year

100,000 min

3 year4 year

180 day

B2

C1

U1

15 yearS2

DE1, E2

C2

E1

LIVB1T2U2

U3

D1 D2S3 S1

T1

LILII

SPOT HRV 1,2,3 and 4 (1998) Pan 10 x10



LIII

1 m0.3 5 100 m10 20

8105

2

3

58

106

1 year

23

5

8107

5 year

10 year

44 day55 day


IRS-1 AB LISS-1 72.5 x 72.5

LISS-2 36.25 x 36.25 IRS-1C

Pan 5 8 x 5 8

2 year

100,000 min

3 year4 year

180 day

B2

C1

U1

15 yearS2

DE1, E2

C2

E1

LIVB1T2U2

U3

D1 D2S3 S1

T1

LILII

SPOT HRV 1,2,3 and 4 (1998) Pan 10 x10



LIII

1 m0.3 5 100 m10 20Utility Infrastructure

102

103

104

5

2

3

5

2

3

5

2

3

5

9 day

1 day

1 hr

12 hr

8

8

8

3 day

2 day

4 day

26 day

44 day


GAC 4 x 4 km

RADARSAT 11 x 9

100 x 100




Pan 1 x 1 MSS 4 x 4

IRS-P5 (1999) Pan 2.5 x 2.5

ORBIMAGE


22 day 16 day

Pan 5.8 x 5.8 LISS-3 23 x 23; MIR 70 x 70

1,000 min

10,000 min

100 min

5 day

30 day


LANDSAT 7 (1998) Pan 15 x 15


Pan 3 x 3 MSS 15 x 15

Quickbird (1998) 0.82 x 0.82 3.28 x 3.28

M1GOES

VIS 0.9 X 0.9 km TIR 8.0 X 8.0 km

DE2

DE3 DE4 DE5

M5

102

103

104

5

2

3

5

2

3

5

2

3

5

9 day

1 day

1 hr

12 hr

8

8

8

3 day

2 day

4 day

26 day

44 day


GAC 4 x 4 km

RADARSAT 11 x 9

100 x 100




Pan 1 x 1 MSS 4 x 4

IRS-P5 (1999) Pan 2.5 x 2.5

ORBIMAGE


22 day 16 day

Pan 5.8 x 5.8 LISS-3 23 x 23; MIR 70 x 70

1,000 min

10,000 min

100 min

5 day

30 day


LANDSAT 7 (1998) Pan 15 x 15


Pan 3 x 3 MSS 15 x 15

Quickbird (1998) 0.82 x 0.82 3.28 x 3.28

M1GOES

VIS 0.9 X 0.9 km TIR 8.0 X 8.0 km

DE2

DE3 DE4 DE5

M5

Tem

pora

l Res

olut

ion

0.2 1.0 2 3 5 10 2 3 5 102 103 1042 3 5 2 3 5

10

2

3

5

8

23

5

30 min

10 min

1 m 10 30 100 m 1 km

1000 m 5 km 10 km

.8.5

5 min

3 min

532 15 20

4 8


4 x 4 km


TIR 5 x 5 km


T3

T4

M3

M4

0.3

M2

0.2 1.0 2 3 5 10 2 3 5 102 103 1042 3 5 2 3 5

10

2

3

5

8

23

5

30 min

10 min

1 m 10 30 100 m 1 km

1000 m 5 km 10 km

.8.5

5 min

3 min

532 15 20

4 8


4 x 4 km


TIR 5 x 5 km


T3

T4

M3

M4

0.3

M2


West Berlin, Germany (1:3,000). Utility companies often digitize the location of every pole, manhole, transmission

line and the facilities associated with each.


U1 - general utility centerline 1 - 5 years 1 - 2 mU2 - precise utility line width 1 - 2 years 0.3 - 0.6 m U3 - locate poles, manholes, substations 1 - 2 years 0.3 - 0.6 m

1212

58

105

2

3

58

106

1 year

23

5

8107

5 year

10 year

44 day 55 day


IRS-1 AB LISS-1 72.5 x 72.5

LISS-2 36.25 x 36.25 IRS-1C

Pan 5.8 x 5.8 LISS-3 23 x 23; MIR 70 x 70

2 year

100,000 min

3 year4 year

30 day

180 day

B2

C1

U1

15 yearS2

DE1, E2

C2

E1

LIVB1T2U2

U3

D1 D2S3 S1

T1

LILII

SPOT HRV 1,2,3 and 4 (1998) Pan 10 x10



LIII

1 m0.3 5 100 m10 20

58

105

2

3

58

106

1 year

23

5

8107

5 year

10 year

44 day 55 day


IRS-1 AB LISS-1 72.5 x 72.5

LISS-2 36.25 x 36.25 IRS-1C

Pan 5.8 x 5.8 LISS-3 23 x 23; MIR 70 x 70

2 year

100,000 min

3 year4 year

30 day

180 day

B2

C1

U1

15 yearS2

DE1, E2

C2

E1

LIVB1T2U2

U3

D1 D2S3 S1

T1

LILII

SPOT HRV 1,2,3 and 4 (1998) Pan 10 x10



LIII

1 m0.3 5 100 m10 20Digital Elevation Model Creation

102

103

104

5

2

3

5

2

3

5

2

3

9 day

1 day

1 hr

12 hr

8

8

8

3 day

2 day

4 day

26 day


GAC 4 x 4 km

RADARSAT 11 x 9

100 x 100




Pan 1 x 1 MSS 4 x 4

IRS-P5 (1999) Pan 2.5 x 2.5

ORBIMAGE


22 day 16 day

LISS 3 23 x 23; MIR 70 x 70

1,000 min

10,000 min

100 min

5 day

30 day


LANDSAT 7 (1998) Pan 15 x 15


Pan 3 x 3 MSS 15 x 15

Quickbird (1998) 0.82 x 0.82 3.28 x 3.28

M1

M2

GOES VIS 0.9 X 0.9 km TIR 8.0 X 8.0 km

DE2

DE3 DE4 DE5

M5

102

103

104

5

2

3

5

2

3

5

2

3

9 day

1 day

1 hr

12 hr

8

8

8

3 day

2 day

4 day

26 day


GAC 4 x 4 km

RADARSAT 11 x 9

100 x 100




Pan 1 x 1 MSS 4 x 4

IRS-P5 (1999) Pan 2.5 x 2.5

ORBIMAGE


22 day 16 day

LISS 3 23 x 23; MIR 70 x 70

1,000 min

10,000 min

100 min

5 day

30 day


LANDSAT 7 (1998) Pan 15 x 15


Pan 3 x 3 MSS 15 x 15

Quickbird (1998) 0.82 x 0.82 3.28 x 3.28

M1

M2


DE2

DE3 DE4 DE5

M5

Tem

pora

l Res

olut

ion

Original Panchromatic Aerial Photograph Digital Elevation Model (DEM)

Observation point

Original Panchromatic Aerial Photograph Digital Elevation Model (DEM)

Observation point

0.2 1.0 2 3 5 10 2 3 5 102 103 1042 3 5 2 3 5

10

2

3

5

8

23 30 min

10 min

1 m 10 30 100 m 1 km

1000 m 5 km 10 km

.8.5

5 min

3 min

532 15 20

4 8


4 x 4 km


TIR 5 x 5 km


T3

T4

M3

M4

0.3

M2

0.2 1.0 2 3 5 10 2 3 5 102 103 1042 3 5 2 3 5

10

2

3

5

8

23 30 min

10 min

1 m 10 30 100 m 1 km

1000 m 5 km 10 km

.8.5

5 min

3 min

532 15 20

4 8


4 x 4 km


TIR 5 x 5 km


T3

T4

M3

M4

0.3

M2

Urban DEMs are usually created from high spatial resolution data. The DEM and orthophoto of Columbia, SC were produced from 1:6,000 stereoscopic photography using soft-copy photogrammetric techniques.


Orthophoto Draped Over DEM

Cellular Transciever Location Model

Orthophoto Draped Over DEM

Cellular Transciever Location Model

Remote Sensing Assisted Population Estimation

Population estimation can be performed at the local, regional, and national level based on (Lo, 1995; Haacket al., 1997):

• counts of individual dwelling units,

• measurement of land areas, and

• land use classification.

1313

Remote Sensing Assisted Population Estimation

Dwelling Unit Estimation Technique Assumptions (Lo, 1986; 1995; Haack et al., 1997):

• imagery must be of sufficient spatial resolution (0.3 - 5 m) to identify individual structures even through tree cover and whether they are residential, commercial, or industrial buildings;

• some estimate of the average number of persons per dwelling• some estimate of the average number of persons per dwelling unit must be available, and

• it is assumed all dwelling units are occupied.

Automated building counts

1414

58

105

2

3

58

106

1 year

23

5

8107

5 year

10 year

44 day 55 day


IRS-1 AB LISS-1 72.5 x 72.5

LISS-2 36.25 x 36.25 IRS-1C

Pan 5.8 x 5.8 LISS-3 23 x 23; MIR 70 x 70

2 year

100,000 min

3 year4 year

30 day

180 day

B2

C1

U1

15 yearS2

DE1, E2

C2

E1

LIVB1T2U2

U3

D1 D2S3 S1

T1

LILII

SPOT HRV 1,2,3 and 4 (1998) Pan 10 x10



LIII

1 m0.3 5 100 m10 20

58

105

2

3

58

106

1 year

23

5

8107

5 year

10 year

44 day 55 day


IRS-1 AB LISS-1 72.5 x 72.5

LISS-2 36.25 x 36.25 IRS-1C

Pan 5.8 x 5.8 LISS-3 23 x 23; MIR 70 x 70

2 year

100,000 min

3 year4 year

30 day

180 day

B2

C1

U1

15 yearS2

DE1, E2

C2

E1

LIVB1T2U2

U3

D1 D2S3 S1

T1

LILII

SPOT HRV 1,2,3 and 4 (1998) Pan 10 x10



LIII

1 m0.3 5 100 m10 20Socioeconomic Characteristics

102

103

104

5

2

3

5

2

3

5

2

3

9 day

1 day

1 hr

12 hr

8

8

8

3 day

2 day

4 day

26 day


GAC 4 x 4 km

RADARSAT 11 x 9

100 x 100




Pan 1 x 1 MSS 4 x 4

IRS-P5 (1999) Pan 2.5 x 2.5

ORBIMAGE


22 day 16 day

LISS 3 23 x 23; MIR 70 x 70

1,000 min

10,000 min

100 min

5 day

30 day


LANDSAT 7 (1998) Pan 15 x 15


Pan 3 x 3 MSS 15 x 15

Quickbird (1998) 0.82 x 0.82 3.28 x 3.28

M1

M2


DE2

DE3 DE4 DE5

M5

102

103

104

5

2

3

5

2

3

5

2

3

9 day

1 day

1 hr

12 hr

8

8

8

3 day

2 day

4 day

26 day


GAC 4 x 4 km

RADARSAT 11 x 9

100 x 100




Pan 1 x 1 MSS 4 x 4

IRS-P5 (1999) Pan 2.5 x 2.5

ORBIMAGE


22 day 16 day

LISS 3 23 x 23; MIR 70 x 70

1,000 min

10,000 min

100 min

5 day

30 day


LANDSAT 7 (1998) Pan 15 x 15


Pan 3 x 3 MSS 15 x 15

Quickbird (1998) 0.82 x 0.82 3.28 x 3.28

M1

M2


DE2

DE3 DE4 DE5

M5

Tem

pora

l Res

olut

ion

Konso village in southern Ethiopia

0.2 1.0 2 3 5 10 2 3 5 102 103 1042 3 5 2 3 5

10

2

3

5

8

23 30 min

10 min

1 m 10 30 100 m 1 km

1000 m 5 km 10 km

.8.5

5 min

3 min

532 15 20

4 8


4 x 4 km


TIR 5 x 5 km


T3

T4

M3

M4

0.3

M2

0.2 1.0 2 3 5 10 2 3 5 102 103 1042 3 5 2 3 5

10

2

3

5

8

23 30 min

10 min

1 m 10 30 100 m 1 km

1000 m 5 km 10 km

.8.5

5 min

3 min

532 15 20

4 8


4 x 4 km


TIR 5 x 5 km


T3

T4

M3

M4

0.3

M2


S1 - local population estimation 5 - 7 years 0.3 - 5 mS2 - regional/national population estimation 5 - 15 years 5 - 20 mS3 - quality of life indicators 5 - 10 years 0.3 - 0.5 m


Single and multiple family residences in Columbia, S. C.

1515

Socioeconomic Characteristics

• Population Estimationsp

• Energy Demand and Conservation

• Quality of Life IndicatorsBuildingL tLotAdjacent AmenitiesAdjacent Hazards

Q lit f li i i di t h h l di f il i

Remote Sensing Quality of Living Indicators

Quality of living indicators such as house value, median family income, average number of rooms, average rent, education, and income can be estimated by extracting the following variables from high spatial resolution panchromatic and/or color imagery (Lindgren, 1985; Lo, 1986; 1995; Haack et al., 1997):

• building size (sq. ft.) • lot size (acreage)• existence of a pool (sq. ft.) • vacant lots per city block• frontage (sq. ft.) • distance house is set-back from street• existence of driveways • existence of garages existence of driveways existence of garages• number of autos visible • paved streets (%)• street width (ft.)• health of the landscaping (vegetation index signature)• proximity to manufacturing and/or retail activity.

1616

Human Habitation in Much of the Undeveloped World Usually Requires High Spatial Resolution

Imagery to Estimate Population or Extract Quality of Life Indicators

Farm in the altiplano adjacent to La Paz, Bolivia at 4,100 m above sea level. Grain has been harvested and arranged in rows of sheaves. Piles of stones (cairns) have a light center with a darker border of weeds and shrubs.

New Venice Village, Santa Marta, in the La Magdelena Province of Colombia South America. The people built lake dwellings to be closer to their fishing grounds. Buildings are separated by 10 to 30 m channels to allow boat traffic in all directions.

8105

2

3

58

106

1 year

23

5

8107

5 year

10 year

44 day55 day


IRS-1 AB LISS-1 72.5 x 72.5

LISS-2 36.25 x 36.25 IRS-1C

Pan 5 8 x 5 8

2 year

100,000 min

3 year4 year

180 day

B2

C1

U1

15 yearS2

DE1, E2

C2

E1

LIVB1T2U2

U3

D1 D2S3 S1

T1

LILII

SPOT HRV 1,2,3 and 4 (1998) Pan 10 x10



LIII

1 m0.3 5 100 m10 20

8105

2

3

58

106

1 year

23

5

8107

5 year

10 year

44 day55 day


IRS-1 AB LISS-1 72.5 x 72.5

LISS-2 36.25 x 36.25 IRS-1C

Pan 5 8 x 5 8

2 year

100,000 min

3 year4 year

180 day

B2

C1

U1

15 yearS2

DE1, E2

C2

E1

LIVB1T2U2

U3

D1 D2S3 S1

T1

LILII

SPOT HRV 1,2,3 and 4 (1998) Pan 10 x10



LIII

1 m0.3 5 100 m10 20Disaster Emergency Response

Pre-Hurricane HugoSullivans Is S C

102

103

104

5

2

3

5

2

3

5

2

3

5

9 day

1 day

1 hr

12 hr

8

8

8

3 day

2 day

4 day

26 day

44 day


GAC 4 x 4 km

RADARSAT 11 x 9

100 x 100




Pan 1 x 1 MSS 4 x 4

IRS-P5 (1999) Pan 2.5 x 2.5

ORBIMAGE


22 day 16 day

Pan 5.8 x 5.8 LISS-3 23 x 23; MIR 70 x 70

1,000 min

10,000 min

100 min

5 day

30 day


LANDSAT 7 (1998) Pan 15 x 15


Pan 3 x 3 MSS 15 x 15

Quickbird (1998) 0.82 x 0.82 3.28 x 3.28

M1GOES

VIS 0.9 X 0.9 km TIR 8.0 X 8.0 km

DE2

DE3 DE4 DE5

M5

102

103

104

5

2

3

5

2

3

5

2

3

5

9 day

1 day

1 hr

12 hr

8

8

8

3 day

2 day

4 day

26 day

44 day


GAC 4 x 4 km

RADARSAT 11 x 9

100 x 100




Pan 1 x 1 MSS 4 x 4

IRS-P5 (1999) Pan 2.5 x 2.5

ORBIMAGE


22 day 16 day

Pan 5.8 x 5.8 LISS-3 23 x 23; MIR 70 x 70

1,000 min

10,000 min

100 min

5 day

30 day


LANDSAT 7 (1998) Pan 15 x 15


Pan 3 x 3 MSS 15 x 15

Quickbird (1998) 0.82 x 0.82 3.28 x 3.28

M1GOES

VIS 0.9 X 0.9 km TIR 8.0 X 8.0 km

DE2

DE3 DE4 DE5

M5

Tem

pora

l Res

olut

ion

Sullivans Is., S.C.July 15, 19881 x 1 mpanchromatic

Post-Hurricane HugoOct. 23, 19891 x 1 m

0.2 1.0 2 3 5 10 2 3 5 102 103 1042 3 5 2 3 5

10

2

3

5

8

23

5

30 min

10 min

1 m 10 30 100 m 1 km

1000 m 5 km 10 km

.8.5

5 min

3 min

532 15 20

4 8


4 x 4 km


TIR 5 x 5 km


T3

T4

M3

M4

0.3

M2

0.2 1.0 2 3 5 10 2 3 5 102 103 1042 3 5 2 3 5

10

2

3

5

8

23

5

30 min

10 min

1 m 10 30 100 m 1 km

1000 m 5 km 10 km

.8.5

5 min

3 min

532 15 20

4 8


4 x 4 km


TIR 5 x 5 km


T3

T4

M3

M4

0.3

M2


DE1 - pre-emergency imagery 1 - 5 years 1 - 5 mDE2 - post-emergency imagery 12 hr - 2 days 0.5 - 2 mDE3 - damaged housing stock 1 - 2 days 0.3 - 1 mDE4 - damaged transportation 1 - 2 days 0.3 - 1 mDE5 - damaged utilities 1 - 2 days 0.3 - 1 m


panchromatic

1717

Disaster Emergency Response

Overturned tanker in Anchorage, Alaska.

Earthquake damage near Northridge, California, January 22, 1994. Landslide cutting off Santa Clara River in California.

58

105

2

3

58

106

1 year

23

5

8107

5 year

10 year

44 day 55 day


IRS-1 AB LISS-1 72.5 x 72.5

LISS-2 36.25 x 36.25 IRS-1C

Pan 5.8 x 5.8 LISS-3 23 x 23; MIR 70 x 70

2 year

100,000 min

3 year4 year

30 day

180 day

B2

C1

U1

15 yearS2

DE1, E2

C2

E1

LIVB1T2U2

U3

D1 D2S3 S1

T1

LILII

SPOT HRV 1,2,3 and 4 (1998) Pan 10 x10



LIII

1 m0.3 5 100 m10 20

58

105

2

3

58

106

1 year

23

5

8107

5 year

10 year

44 day 55 day


IRS-1 AB LISS-1 72.5 x 72.5

LISS-2 36.25 x 36.25 IRS-1C

Pan 5.8 x 5.8 LISS-3 23 x 23; MIR 70 x 70

2 year

100,000 min

3 year4 year

30 day

180 day

B2

C1

U1

15 yearS2

DE1, E2

C2

E1

LIVB1T2U2

U3

D1 D2S3 S1

T1

LILII

SPOT HRV 1,2,3 and 4 (1998) Pan 10 x10



LIII

1 m0.3 5 100 m10 20Energy Demand and Conservation

102

103

104

5

2

3

5

2

3

5

2

3

9 day

1 day

1 hr

12 hr

8

8

8

3 day

2 day

4 day

26 day


GAC 4 x 4 km

RADARSAT 11 x 9

100 x 100




Pan 1 x 1 MSS 4 x 4

IRS-P5 (1999) Pan 2.5 x 2.5

ORBIMAGE


22 day 16 day

LISS 3 23 x 23; MIR 70 x 70

1,000 min

10,000 min

100 min

5 day

30 day


LANDSAT 7 (1998) Pan 15 x 15


Pan 3 x 3 MSS 15 x 15

Quickbird (1998) 0.82 x 0.82 3.28 x 3.28

M1

M2


DE2

DE3 DE4 DE5

M5

102

103

104

5

2

3

5

2

3

5

2

3

9 day

1 day

1 hr

12 hr

8

8

8

3 day

2 day

4 day

26 day


GAC 4 x 4 km

RADARSAT 11 x 9

100 x 100




Pan 1 x 1 MSS 4 x 4

IRS-P5 (1999) Pan 2.5 x 2.5

ORBIMAGE


22 day 16 day

LISS 3 23 x 23; MIR 70 x 70

1,000 min

10,000 min

100 min

5 day

30 day


LANDSAT 7 (1998) Pan 15 x 15


Pan 3 x 3 MSS 15 x 15

Quickbird (1998) 0.82 x 0.82 3.28 x 3.28

M1

M2


DE2

DE3 DE4 DE5

M5

Tem

pora

l Res

olut

ion

Daytime high resolution Nighttime 0 3 x 0 3 m

0.2 1.0 2 3 5 10 2 3 5 102 103 1042 3 5 2 3 5

10

2

3

5

8

23 30 min

10 min

1 m 10 30 100 m 1 km

1000 m 5 km 10 km

.8.5

5 min

3 min

532 15 20

4 8


4 x 4 km


TIR 5 x 5 km


T3

T4

M3

M4

0.3

M2

0.2 1.0 2 3 5 10 2 3 5 102 103 1042 3 5 2 3 5

10

2

3

5

8

23 30 min

10 min

1 m 10 30 100 m 1 km

1000 m 5 km 10 km

.8.5

5 min

3 min

532 15 20

4 8


4 x 4 km


TIR 5 x 5 km


T3

T4

M3

M4

0.3

M2


E1 - energy demand and production potential 1 - 5 years 0.3 - 1 mE2 - building insulation surveys 1 - 5 years 1 - 5 m


Daytime high resolution (0.3 x 0.3 m) aerial photography of a

gymnasium

Nighttime 0.3 x 0.3 m thermal infrared

imagery (8 - 14 m)

1818

5 8

105

2

3

58

106

1 year

23

5

8107

5 year

10 year

44 day 55 day


IRS-1 AB LISS-1 72.5 x 72.5

LISS-2 36.25 x 36.25 IRS-1C

Pan 5.8 x 5.8 LISS-3 23 x 23; MIR 70 x 70

2 year

100,000 min

3 year4 year

30 day

180 day

B2

C1

U1

15 yearS2

DE1, E2

C2

E1

LIVB1T2U2

U3

D1 D2S3 S1

T1

LILII

SPOT HRV 1,2,3 and 4 (1998) Pan 10 x10



LIII

1 m0.3 5 100 m10 20

5 8

105

2

3

58

106

1 year

23

5

8107

5 year

10 year

44 day 55 day


IRS-1 AB LISS-1 72.5 x 72.5

LISS-2 36.25 x 36.25 IRS-1C

Pan 5.8 x 5.8 LISS-3 23 x 23; MIR 70 x 70

2 year

100,000 min

3 year4 year

30 day

180 day

B2

C1

U1

15 yearS2

DE1, E2

C2

E1

LIVB1T2U2

U3

D1 D2S3 S1

T1

LILII

SPOT HRV 1,2,3 and 4 (1998) Pan 10 x10



LIII

1 m0.3 5 100 m10 20Critical Environmental Area Assessment

Sun City, S.C.Di iti d

102

103

104

5

2

3

5

2

3

5

2

3

9 day

1 day

1 hr

12 hr

8

8

8

3 day

2 day

4 day

26 day


GAC 4 x 4 km

RADARSAT 11 x 9

100 x 100




Pan 1 x 1 MSS 4 x 4

IRS-P5 (1999) Pan 2.5 x 2.5

ORBIMAGE


22 day 16 day

LISS 3 23 x 23; MIR 70 x 70

1,000 min

10,000 min

100 min

5 day

30 day


LANDSAT 7 (1998) Pan 15 x 15


Pan 3 x 3 MSS 15 x 15

Quickbird (1998) 0.82 x 0.82 3.28 x 3.28

M1

M2


DE2

DE3 DE4 DE5

M5

102

103

104

5

2

3

5

2

3

5

2

3

9 day

1 day

1 hr

12 hr

8

8

8

3 day

2 day

4 day

26 day


GAC 4 x 4 km

RADARSAT 11 x 9

100 x 100




Pan 1 x 1 MSS 4 x 4

IRS-P5 (1999) Pan 2.5 x 2.5

ORBIMAGE


22 day 16 day

LISS 3 23 x 23; MIR 70 x 70

1,000 min

10,000 min

100 min

5 day

30 day


LANDSAT 7 (1998) Pan 15 x 15


Pan 3 x 3 MSS 15 x 15

Quickbird (1998) 0.82 x 0.82 3.28 x 3.28

M1

M2


DE2

DE3 DE4 DE5

M5

Tem

pora

l Res

olut

ion

Digitized NAPPJan. 22, 19942.5 x 2.5 m(0.7 - 0.9 m)

CAMS Band 6

0.2 1.0 2 3 5 10 2 3 5 102 103 104 2 3 5 2 3 5

10

2

3

5

8

2 3 30 min

10 min

1 m 10 30 100 m 1 km

1000 m 5 km 10 km

.8.5

5 min

3 min

532 15 20

4 8


4 x 4 km


TIR 5 x 5 km


T3

T4

M3

M4

0.3

M2

0.2 1.0 2 3 5 10 2 3 5 102 103 104 2 3 5 2 3 5

10

2

3

5

8

2 3 30 min

10 min

1 m 10 30 100 m 1 km

1000 m 5 km 10 km

.8.5

5 min

3 min

532 15 20

4 8


4 x 4 km


TIR 5 x 5 km


T3

T4

M3

M4

0.3

M2


C1 - stable sensitive environments 1 - 2 years 1 - 10 mC2 - dynamic sensitive environments 1 - 6 months 0.5 - 5 m


CAMS Band 6Sept. 23, 19962.5 x 2.5 m(0.7 - 0.69 m)

58

105

2

3

58

106

1 year

23

5

8107

5 year

10 year

44 day 55 day


IRS-1 AB LISS-1 72.5 x 72.5

LISS-2 36.25 x 36.25 IRS-1C

Pan 5.8 x 5.8 LISS-3 23 x 23; MIR 70 x 70

2 year

100,000 min

3 year4 year

30 day

180 day

B2

C1

U1

15 yearS2

DE1, E2

C2

E1

LIVB1T2U2

U3

D1 D2S3 S1

T1

LILII

SPOT HRV 1,2,3 and 4 (1998) Pan 10 x10



LIII

1 m0.3 5 100 m10 20

58

105

2

3

58

106

1 year

23

5

8107

5 year

10 year

44 day 55 day


IRS-1 AB LISS-1 72.5 x 72.5

LISS-2 36.25 x 36.25 IRS-1C

Pan 5.8 x 5.8 LISS-3 23 x 23; MIR 70 x 70

2 year

100,000 min

3 year4 year

30 day

180 day

B2

C1

U1

15 yearS2

DE1, E2

C2

E1

LIVB1T2U2

U3

D1 D2S3 S1

T1

LILII

SPOT HRV 1,2,3 and 4 (1998) Pan 10 x10



LIII

1 m0.3 5 100 m10 20Meteorological Data

102

103

104

5

2

3

5

2

3

5

2

3

9 day

1 day

1 hr

12 hr

8

8

8

3 day

2 day

4 day

26 day


GAC 4 x 4 km

RADARSAT 11 x 9

100 x 100




Pan 1 x 1 MSS 4 x 4

IRS-P5 (1999) Pan 2.5 x 2.5

ORBIMAGE


22 day 16 day

LISS 3 23 x 23; MIR 70 x 70

1,000 min

10,000 min

100 min

5 day

30 day


LANDSAT 7 (1998) Pan 15 x 15


Pan 3 x 3 MSS 15 x 15

Quickbird (1998) 0.82 x 0.82 3.28 x 3.28

M1

M2


DE2

DE3 DE4 DE5

M5

102

103

104

5

2

3

5

2

3

5

2

3

9 day

1 day

1 hr

12 hr

8

8

8

3 day

2 day

4 day

26 day


GAC 4 x 4 km

RADARSAT 11 x 9

100 x 100




Pan 1 x 1 MSS 4 x 4

IRS-P5 (1999) Pan 2.5 x 2.5

ORBIMAGE


22 day 16 day

LISS 3 23 x 23; MIR 70 x 70

1,000 min

10,000 min

100 min

5 day

30 day


LANDSAT 7 (1998) Pan 15 x 15


Pan 3 x 3 MSS 15 x 15

Quickbird (1998) 0.82 x 0.82 3.28 x 3.28

M1

M2


DE2

DE3 DE4 DE5

M5

Tem

pora

l Res

olut

ion

0.2 1.0 2 3 5 10 2 3 5 102 103 1042 3 5 2 3 5

10

2

3

5

8

23 30 min

10 min

1 m 10 30 100 m 1 km

1000 m 5 km 10 km

.8.5

5 min

3 min

532 15 20

4 8


4 x 4 km


TIR 5 x 5 km


T3

T4

M3

M4

0.3

M2

0.2 1.0 2 3 5 10 2 3 5 102 103 1042 3 5 2 3 5

10

2

3

5

8

23 30 min

10 min

1 m 10 30 100 m 1 km

1000 m 5 km 10 km

.8.5

5 min

3 min

532 15 20

4 8


4 x 4 km


TIR 5 x 5 km


T3

T4

M3

M4

0.3

M2


M1 - daily weather prediction 30 min - 12 hr 1 - 8 kmM2 - current temperature 30 min - 1 hr 1 - 8 kmM3 - current precipitation 10 min - 30 min 4 x 4 kmM4 - immediate severe storm warning 5 min - 10 min 4 x 4 kmM5 - monitoring urban heat islands 12 - 24 hr 5 x 10 m


GOES East image of Hurricane Hugo 2:44 p.m. EDT Sept. 21, 1989

1919

R S i d G hi

Vilmaliz Rodríguez GuzmánM.S. Student, Department of GeologyUniversity of Puerto Rico at Mayagüez

Remote Sensing and Geographic Information Systems (GIS)

Reference:James B. Campbell. Introduction to Remote Sensing. 4th. New

York & London: The Guilford Press, 2007.

What is GIS ?

Geographic information systems are specialized computer programs designed to storagecomputer programs designed to storage,

manipulates, display and analyze geospatial data.

2020

How does it work?

GIS Components

2121

Raster

o The region of interest is subdivided into a network of such cells

Basic data structures

o The region of interest is subdivided into a network of such cells of uniform size and shape; each unit in then encoded with a single category or value (attribute)

o Remote Sensing data are collected and presented in raster format.

VectorIt is stored as points lines and polygons

Basic data structures

It is stored as points, lines and polygons.o Points- X and Y coordinates

o Lines- connected points

o Polygons- line features that are connected to form an area

2222

Examples of data that can be integrated in a GIS

o Orthorectified Aerial photos

S lli io Satellite images

Image products

o Digital Elevation Models (DEMs)

o Demographic data

o Physical features

Soil types

Geology

o Research field data

2323

Specialized programs adapted for the manipulation of geographic data

Image display

GIS Software

g p y

Display data in a map-like format so that geographic patterns and interrelationships are visible

Overlay CapabilityVisual overlay

Superimpose two (or more) layers on the screen so that the two patterns can be seen together in a single image.

L i l d i h i lLogical and arithmetic overlay

Analyst can define new variables or categories based upon the matching of different overlays at each point of the map

Projection conversionProvides de ability to change from one map projection or geographic reference system to another

A mathematical model that transform locations on the globe (curve surface) to locations on a two

Map Projections

the globe (curve surface) to locations on a two-dimensional (flat) surface

Cause DistortionsArea

Distance

Shape

Direction

2424

Examples:Geographic Coordinate System (GCS)-Lat/Long

Spatial Reference SystemsSpatial Reference Systems

g p y ( ) / g State Plane Coordinate System – will be in either feet or metersUniversal Transverse Mercator AlbersLambert

How to choose the best spatial reference systems?p y Choose the projection that better preserve the properties to be analyze Evaluate the more common map projection used with the data-set to be

used

Define vs. Project vs. Re-project

o Intergraph Corporation

o IDRISI

Examples of Software

o MapInfo

o GRASS

o ArcGIS…

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ArcGISo Latest version: 9.3

o Three levels of license: ArcInfo, ArcEditord A Viand ArcView

o Applications:ArcMap- used to create maps, view, edit, and analyze

spatial data.

ArcScene- allows you to overlay many layers of data i 3D iin a 3D environment

ArcTool box- has tools for geoprocessing, data conversion, and defining and changing map projections

ArcCatalog-used to manage and organize GIS data, preview datasets, view and manage metadata.

ArcGIS-Basic Terms

o Attribute Table- are associated with a class of geographic features, such as wells or roads Each row represents a geographic featuresuch as wells or roads. Each row represents a geographic feature.

o Geo-databases- the common data storage and management framework for ArcGIS and can be utilized wherever it is needed

o Coverage- A spatial dataset containing a common feature type

o Shapefiles- A set of files that contain a set of points, arcs, or polygons (or features) that hold tabular data and a spatial location. Files: *.shp, *.sbx, *.sbn, *.dbh, *.prj

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Examples of GIS Examples of GIS applicationsapplications

Preparing a GIS for an area of interest (Browse and download data using different resources, re-project, categorize and clip layers, add

data to attribute tables)

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Importing bathymetric (XYZ) data(Prepare table using Excel, Import using “Add XY data” tool)

Generating raster surfaces and contours from point data(Spatial Analyst extension: Topo to Raster and Contour tools)

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Incorporating field data to a GIS(Use of GPS unit)

Data set preparation for interpolation analysis

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Example: Changes in salinity along the Mayagüez Bay(Spatial Analyst: Interpolate to raster)

Maps preparation

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Visualization of layers using 3D approach(3D Analyst: ArcScene)

bb620 at 1 meterbb620 at 1 meter

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bb620 at 2 metersbb620 at 2 meters

bb620 at 3 metersbb620 at 3 meters

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Example: Shoreline changes analysis(Georeferentiation, Digitalization, measurement tools)

Alejandra Alejandra RodríguezRodríguez, undergraduate research, undergraduate research

Example: Spatial distribution of earthquake damage risk(Dissolve & merge layers, Reclassify raster files, Spatial Analyst: Raster Calculator)

Ivelisse Lopez, undergraduate research

Documents

Ch13-RS Urban Landscape - gers.uprm.edu · Urban Landscape REFERENCE: Remote Sensing of the Environment John R. Jensen (2007) Second Edition Pearson Prentice Hall Urban Remote Sensing