Remote sensing –Beyond images Mexico 14-15 December 2013 The workshop was organized by CIMMYT Global Conservation Agriculture Program (GCAP) and funded by the Bill & Melinda Gates Foundation (BMGF), the Mexican Secretariat of Agriculture, Livestock, Rural Development, Fisheries and Food (SAGARPA), the International Maize and Wheat Improvement Center (CIMMYT), CGIAR Research Program on Maize, the Cereal System Initiative for South Asia (CSISA) and the Sustainable Modernization of the Traditional Agriculture (MasAgro)

1. DigitalGlobe Overview
December 2013
Kumar Navulur, PhD
1

2. DigitalGlobe Constellation
Quickbird
GeoEye-1
WorldView-1
WorldView-2
IKONOS
0.82 m GSD
150,000 km2 /day
WorldView-3
0.65 m GSD
200,000 km2 /day
0.41 m GSD
700,000 km2 /day
0.5 m GSD
1,500,000 km2 /day
0.46 m GSD
1,200,000 km2 /day
2014
0.31 m GSD
680,000 km2 /day
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3. DigitalGlobe Archive
4
2
0
0
0
0
0
0
0
0
Sq Km
3

4. WorldView2 & 3 VNIR and CAVIS VNIR bands are
optimized for different missions
1.0
0.9
yellow
590-630
coastal
400-452
blue
448-510
green
514-586
red
632-692
red-edge
706-746
NIR1
772-890
NIR2
866-954
0.8
WorldView2/3 – VNIR Bands
0.7
0.6
LDCM Panchromatic
LDCM
0.5
0.4
LDCM Blue
LDCM Green
LDCM Red
LDCM NIR
WorldView3 - CAVIS VNIR Bands
897-927
water-2
0.3
0.2
0.1
405-420
Desert Clouds
459-479
Blue
Aerosol-1
525-585
green
635-685
Red
Aerosol-2
845-885
water-1
930-965
water-3
0.0
0.40 0.42 0.44 0.46 0.48 0.50 0.52 0.54 0.56 0.58 0.60 0.62 0.64 0.66 0.68 0.70 0.72 0.74 0.76 0.78 0.80 0.82 0.84 0.86 0.88 0.90 0.92 0.94 0.96 0.98 1.00
Aeronet 10nm Bands
4

5. WorldView3 SWIR and CAVIS3 SWIR bands are
optimized for different missions
1.0
WorldView3 – 8 SWIR Bands
0.9
0.8
1640-1680
MS11
2145-2185 2235-2285
MS13
MS15
0.7
0.6
1195-1225
MS9
0.5
0.4
1550-1590
MS10
LDCM
Cirrus
1710-1750
MS12
2185-2225
MS14
2295-2365
MS16
LDCM SWIR 2
LDCM SWIR 1
WorldView3 - CAVIS3 SWIR Bands
2105-2245
Aerosol-3
0.3
0.2
0.1
1370-1410
CIRRUS
1620-1680
snow / cloud
1220-1252
Aerosol-NDVI
2105-2245
Aerosol-3
0.0
1.00 1.05 1.10 1.15 1.20 1.25 1.30 1.35 1.40 1.45 1.50 1.55 1.60 1.65 1.70 1.75 1.80 1.85 1.90 1.95 2.00 2.05 2.10 2.15 2.20 2.25 2.30 2.35 2.40
Aeronet 10nm Bands
25nm
5

6. Satellite Spectral Comparison
MODIS
(250+ m)
ASTER
2 (R)
(15/30 m)
4 (NIR) backward
(1.2 / 3.7)
Coastal
CAVIS
(30 m)
300
3 (NIR) nadir
Red
Blue Yellow Edge
Green Red
Aerosol 1
Desert
Cloud
7 (SWIR 3)
9 (SWIR 5)
Stereo
1 (G)
WV-3
5 (SWIR 1)
6 (SWIR 2)
NIR 2
SWIR 2
NIR 1
Aerosol 2
SWIR 1
Water 2
SWIR 4
SWIR 3
NDVI - SWIR
Snow
10 (SWIR 6)
8
(SWIR4)
SWIR 6
SWIR 5
SWIR 8
SWIR 7
Aerosol 3
Cloud Height
Parallax
Green
500
Wavelength in nanometers
Water 1
700
Water 3
900
Cirrus
Aerosol 3
1100 1300 1500 1700 1900 2100 2300 2500
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6

7. CAMP processor enables high quality information &
insight extraction, and multi-temporal analysis-1
Color image of a typical 21km x 17km scene
Addressed Effects
• Opaque clouds
• Cirrus clouds
• Aerosols
• Water vapor
• Ice/Snow
• Shadows
• BRDF
Core Products
• Reflectance imagery
accurate to within 1%
absolute
• Utility masks located to
~1m CE90/LE90
4 km
visibility
11 km
7

8. CAMP processor enables high quality information &
insight extraction, and multi-temporal analysis-2
Aerosol variability on a typical 21km x 17km scene
Addressed Effects
• Opaque clouds
• Cirrus clouds
• Aerosols
• Water vapor
• Ice/Snow
• Shadows
• BRDF
Core Products
• Reflectance imagery
accurate to within 1%
absolute
• Utility masks located to
~1m CE90/LE90
4 km
visibility
11 km
8

9. CAMP processor enables high quality information &
insight extraction, and multi-temporal analysis-3
Atmospherically corrected color image of a typical 21km x 17km scene
Addressed Effects
• Opaque clouds
• Cirrus clouds
• Aerosols
• Water vapor
• Ice/Snow
• Shadows
• BRDF
Core Products
• Reflectance imagery
accurate to within 1%
absolute
• Utility masks located to
~1m CE90/LE90
4 km
visibility
11 km
9

10. Where are we headed: Enable Automated
Information Extraction
Water vapor
Distortion
Reflectance or Radiance
Enables
developing
spectral models
for automated
information
extraction
Reflectance
Aerosol
Distortion
Wavelength (nm)
Blue
Green
Red
Infrared
DigitalGlobe Proprietary and Business Confidential
10

11. NDVI values from TOA and surface reflectance
Water Vapor absorption
0.887
(~10%)
0.894
(~13%)
0.811
0.789
Scattering
11

12. NDVI from TOA reflectance
Longmont (August 10, 2011 – WV2)
NDVI from TOA reflectance
no vegetation
vigorous vegetation
12

13. NDVI from Surface Reflectance
Longmont (August 10, 2011 – WV2)
NDVI from surface reflectance
LAI: 0.56 [m2 /m2]
LAI: 4.53 [m2 /m2]
LAI: 3.72 [m2 /m2]
LAI: 4.45 [m2 /m2]
no vegetation
vigorous vegetation
13

14. Time-Series Data Set
The data set used is composed of 21 images
acquired between 2002 and 2009 by
QuickBird over the city of Denver, Colorado.
The time-series covers part of the downtown
area and includes single family houses,
skyscrapers, apartment complexes, industrial
buildings, roads/highways, urban parks, and
bodies of water.
14

15. Change Detection
Denver – QB: July 17, 2002
Surface Refl. Change Detection
DN Change Detection
Denver – QB: August 22, 2008
Negative
Change
No-Change
Positive
Change
15

16. Time-series over Longmont, CO (2009-2012)
Date
meanSunAz meanSunEl meanSatAz meanSatEl
2010-06-05T18:02:26
141.0
68.9
165.1
71.8
2010-06-18T18:27:12
153.6
71.9
324.8
51.0
2010-06-24T18:09:52
141.9
70.0
218.4
80.9
2010-11-03T18:10:20
170.1
34.3
16.6
72.4
2010-11-03T18:10:30
170.2
34.3
18.0
78.0
2010-11-03T18:10:41
170.2
34.3
21.9
83.8
2010-11-03T18:10:52
170.3
34.3
93.3
89.0
2010-11-03T18:11:12
170.4
34.4
190.0
78.5
2011-03-29T17:59:02
153.4
50.6
77.3
62.6
2011-03-29T17:59:10
153.4
50.6
85.7
63.8
2011-03-29T17:59:21
153.5
50.6
98.1
64.7
2011-03-29T17:59:29
153.5
50.6
108.6
64.6
2011-03-29T17:59:40
153.6
50.6
120.8
63.5
2011-06-16T18:09:27
142.7
70.2
19.2
35.6
2011-08-09T18:33:50
161.6
64.9
248.9
57.6
2011-08-10T17:58:24
144.1
61.5
156.5
40.8
2011-08-10T17:58:15
144.1
61.5
153.6
43.0
2011-08-10T17:58:06
144.0
61.4
150.3
45.3
2011-08-20T18:30:05
162.5
61.5
258.4
66.5
2011-08-23T18:19:36
158.4
59.9
339.1
82.0
2011-08-23T18:20:10
158.6
59.9
214.7
77.4
2011-08-23T18:20:19
158.7
59.9
208.9
72.6
2011-09-22T18:18:55
166.8
49.6
0.9
77.6
2012-01-26T17:48:00
157.3
28.1
63.0
39.9
2012-04-13T18:18:10
159.9
58.0
115.4
87.0
2012-04-18T18:33:47
167.0
60.6
288.3
66.9
WV1 Image CatID
1 1020010009353000
2 10200100094A3700
3 10200100098B0A00
4 102001000A08B800
5 102001000A68D900
6 102001001109C500
7 10200100180FDD00
8 1020010018D32200
Date
meanSunAz meanSunEl meanSatAz meanSatEl
2009-10-18T18:14:10
170.0
39.7
238.0
70.9
2009-10-18T18:14:05
169.9
39.7
250.5
73.9
2009-10-18T18:13:55
169.9
39.6
283.8
76.4
2009-10-18T18:13:44
169.8
39.6
316.9
73.8
2009-10-18T18:13:39
169.8
39.6
327.9
71.2
2011-03-29T18:30:23
165.6
52.6
281.2
49.2
2012-01-09T18:07:03
164.4
26.4
185.0
52.2
2012-01-09T18:06:09
164.1
26.3
64.9
83.4
345
70
0
WV2
QB
WV1
Sun
15
330
30
60
315
50
40
45
300
60
30
20
zenith (°)
WV2 Image CatID
1 1030010005098C00
2 103001000582F000
3 10300100051AFE00
4 103001000784E200
5 10300100079DC100
6 103001000760B200
7 10300100077FC700
8 103001000701FC00
9 103001000913F200
10 103001000A847800
11 103001000AA58D00
12 103001000A655E00
13 103001000A905200
14 103001000B335B00
15 103001000C1D2D00
16 103001000CC40500
17 103001000D763100
18 103001000CC55900
19 103001000D46A300
20 103001000D824500
21 103001000DB4AA00
22 103001000D272200
23 103001000D7FF200
24 10300100111AD800
25 10300100129FE800
26 1030010013B5B300
285
75
10
0
270
90
10
20
255
105
30
40
50
QB Image
CatID
1 101001000A76CD00
2 101001000925E300
3 1010010009086100
Date
meanSunAz meanSunEl meanSatAz meanSatEl
2009-10-18T17:50:58
162.7
38.6
66.1
70.7
2009-02-05T18:09:05
161.5
32.3
263.3
79.1
2009-01-08T17:59:34
162.5
25.9
87.1
69.1
240
120
225
135
60
70
210
150
195
180
165
16

17. WV2: DG-AComp accuracy
0.50
Field measurement
DG-AComp
RMSE (MS)
concrete
concrete
0.0070
asphalt
0.45
0.0127
0.40
Reflectance
0.35
0.30
0.25
0.20
0.15
RMSE (PAN)
0.10
concrete
asphalt
asphalt
0.05
0.0013
0.0018
0.00
C
B
G
Y
R
RE
N1
N2
PAN
17

18. Calibrated Tarps
The tarps were manufactured to guarantee a flat spectral response between
400 nm and 1050 nm, with a peek-to-peek variation in reflectance less than
10% between 10° and 60° off-nadir [3][4].
[3] M. Pagnutti, K. Holekamp, R.E. Ryan, R.D. Vaughan, J.A. Russell, D. Prados, and T. Stanley, “Atmospheric
correction of high spatial resolution commercial satellite imagery products using modis atmospheric products”,
in Analysis of Multi-Temporal Remote Sensing Images, May 2005, pp. 115 – 119.
[4] K. Holekamp, “NASA radiometric characterization”, in High spatial resolution commercial imagery workshop,
Reston, VA, Nov. 2004
18

19. Data Set
Location
NASA Stennis Space Center, MS
NASA Stennis Space Center, MS
NASA Stennis Space Center, MS
NASA Stennis Space Center, MS
Brookings, SD
Brookings, SD
Brookings, SD
Brookings, SD
Wiggins, MS
Wiggins, MS
Wiggins, MS
Park Falls, WI
Date
7-Feb-06
10-Jan-04
14-Nov-02
12-Mar-05
15-Sep-03
7-Sep-02
20-Jul-02
18-Oct-05
15-Mar-06
7-Jan-06
25-Jan-06
5-Aug-05
Sat. Az.
208.2
259.7
274.2
268.4
284.4
191.4
349.7
298.2
319.5
300.0
296.6
261.5
Sat. El.
83.2
88.2
79.2
77.7
83.1
74.6
64.3
73.2
76.7
68.0
69.3
69.4
This set of 12 QuickBird images includes four 20 m2 spectrally-flat tarps having
nominally 3.5, 22, 34, and 52% reflectance in the visible through NIR spectral
region.
19

20. QB: DG-AComp accuracy
(52, 34, 22, and 3.5% reflectance)
1.0
DG-AComp
RMSE (MS)
52%
0.8
0.0155
34%
0.0127
22%
0.0155
3.5%
0.9
0.0082
Reflectance
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0.0
B
G
R
NIR
20

21. Information Extraction: Exploiting various
dimensions of imagery
Spectral
Spatial/
Morphological
Temporal
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21

22. Information Extraction: Model Portability
Istanbul – WV02: Feb. 19, 2010
Honolulu – WV02: Apr. 25, 2010
Rio de Janeiro – WV02: Jan. 19, 2010
New York – WV02: Dec. 18, 2009
Six classes of interest:
1. Grass
2. Tree
3. Water
4. Soil
5. Built-up
6. Shadow
22

23. Information Extraction: Model Portability
Three different experiments using training pixels from:
1. Honolulu
2. Honolulu and Istanbul
3. Honolulu, Istanbul, and New York
DN
In all cases, the image of Rio de Janeiro has been used
ONLY for validation.
DN
Kappa Coefficient
Surf. Refl.
Surf. Refl.
1
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
Honolulu
Honolulu, Istanbul
Honolulu, Istanbul,
NewYork
23