The document describes mapping crop intensity and characterizing stress prone areas in Odisha, India using remote sensing. Key findings include:
1) Satellite imagery was used to map cropping patterns in Odisha, identifying different rice cropping classes based on irrigation and season.
2) Temporal signatures from satellite data helped differentiate cropping systems like irrigated single crop rice, irrigated double crop rice/rice, and rainfed single crop rice.
3) Spatial modeling integrated bio-physical data from remote sensing with household survey data to analyze relationships between agricultural production and socioeconomic factors.
3. Results: cropping pattern of Odisha
Kharif-autumn (2010-11) Kharif-winter (2010-11) Rabi-summer (2010-11)
Legend
Total kharif-autumn-Irrigated-rice (class 6,7) 884,292 ha Districts
Kharif-winter-rainfed rice (classes 1,2 and 3) 2,408,664 ha 70% 01. Irrigated-SW/GW-rice
02. Irrigated-SW-rice
Kharif-winter-irrigated rice (classes 4,6 and 7) 1,054,899 ha 30%
03. Irrigated-GW-rice
Total kharif-autumn-rice 3,463,562 ha 04. Rainfed - rice
Rabi-summer-Irrigated rice (classes 6 and 7) 442,146 ha Other classes
Total rice area (kharif + rabi) 4,790,000 ha
4. Methodology: Drought/Submergence mapping
MODIS 8- day
7b data for the
year 2010:
322 bands
Google earth
Irrigated, rice first crop, other
0.8
crops second crop, double crop
MFDC has 344 layers, for a
0.6
NDVI
total data volume of 84.5 0.4
gigabytes 0.2
0.0
Jun-10 Sep-10 Dec-10 Apr-11
Date
5. Temporal signatures for different rice systems
irrigated - single crop - rice irrigated - double crop - rice/rice
irrigated - double crop - rice/other rainfed - single crop - rice
Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr May
1.0
0.9
0.8
0.7
Vegetation index
0.6
0.5
0.4
0.3
0.2
0.1
0.0
6. Results: cropping pattern of Odisha
Gumma MK, Mohanty S, Nelson A, Rala A, and Das SR (review) Improving Rice Production in Odisha:
A Key to Achieving India's Future Food Security. PLoS ONE. (Submitted in Apr2012).
7. Results: cropping pattern of Odisha
Gumma MK, Mohanty S, Nelson A (2012) Identifying stress prone villages using time series satellite
imagery: A case study on Odisha. AIPA 2012, 58-64.
8. Dissemination
Sundargarh
Jharsuguda Mayurbhanj
Keonjhar Baleshwar
Deogarh
Baragarh Sambalpur
Sonepur Bhadrak
Angul Dhenkanal
Boudh Jajpur
Bolangir
Nuapada CuttackKendrapara
Nayagarh Jagatsinghpur
Khordha
Kandhamal Puri
Kalahandi
Ganjam
Nabarangpur
Rayagada
Gajapati
Koraput Legend
Land use change by Land use change by
Districts
submergence drought
Malkangiri Major roads Once Once
2-3 2-3
>3 >3
9. 2010-11
2009-10
2004-05
2002-03
2008-09
2003-04
2006-07
2000-01
2005-06
2001-02
2007--08
2007--08
Sundargarh
Sundargarh
Sundargarh
S n ag r
u d r ah
Jharsuguda
Jharsuguda
Jharsuguda
J as g d
h ru u a Mayurbhanj
Mayurbhanj
Mayurbhanj
Mayurbhanj
Myrh n
a ub a j
Keonjhar
Keonjhar
Keonjhar
Keonjhar
Kohr
ena
j
Sambalpur Deogarh
Sambalpur Deogarh
Sambalpur Deogarh
Sm au
a bl r
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Baleshwar
Baleshwar
Bl swr
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e
Baragarh
Baragarh
Baragarh
Baragarh
Brg r
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Sonepur
Sonepur
Sonepur
Sn pr
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Bhadrak
Bhadrak
Ba rk
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Angul Dhenkanal Jajpur
Angul Dhenkanal Jajpur
Angul Dhenkanal Jajpur
Ag l
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Boudh
Boudh
Boudh
Bu h
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Kendrapara
Kn rpr
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Kendrapara
Bolangir
Bolangir
Bolangir
Bl nr
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Kendrapara
Kendrapara
Kn rpr
e da aa
Nuapada
Nuapada
Nuapada
Naaa
upd Cuttack
Cuttack
Cuttack
C ta k
uc
Nayagarh
Nayagarh
Nayagarh
Nygr
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Jagatsinghpur
Jagatsinghpur
J g tn h u
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s
Jagatsinghpur
Kandhamal
Kandhamal
Kandhamal
Kandhamal
Kn hm l
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Khordha Puri PuriJagatsinghpur
Khordha Puri Puri Jagatsinghpur
Khordha Puri Puri Jagatsinghpur
Khordha P r
K od a
hrh ui Puri Jagatsinghpur
P r J g tn h u
ui a a i g p r
s
Kalahandi
Kalahandi
Kalahandi
Kl hni
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a Puri
Puri
Puri
Puri
Pr
ui Legend
Ganjam
Ganjam
Ganjam
Gnm
aa
j
Nabarangpur
Nabarangpur
Nabarangpur
Nbrnpr
a aa g u Districts
Rayagada
Rayagada
Rayagada
Rayagada
Rygd
aa a a Builtup areas
Gajapati Ganjam
Gajapati Ganjam
Ga a
ap t
j i
Gajapati Ganjam
Gajapati G n m
aaj Rice growingareas
Koraput
Koraput
Koraput
Krp t
oa u Moderate drought
Severe drought
Submergence
Malkangiri
Malkangiri
Malkangiri
M l a gi
a nr
k i
800
800 0.9
0.9
700
700 0.8
0.8
0.7
0.7
600
TRMM rainfall rainfall (mm)
600
0.6
0.6
500
500
TRMM (mm)
0.5
0.5
NDVI
400
400
0.4
0.4
300
300
0.3
0.3
NDVI
200
200 0.2
0.2
100
100 0.1
0.1
0
0 0
0
15-Jan- 15-Jul- 15-Jan- 15-Jul- 15-Jan- 15-Jul- 15-Jan- 15-Jul- 15-Jan- 15-Jul- 15-Jan- 15-Jul- 15-Jan- 15-Jul- 15-Jan- 15-Jul- 15-Jan- 15-Jul- 15-Jan- 15-Jul- 15-Jan-15-Jul- 15-Jan-
15- 15-Jul- 15- 15-Jul- 15- 15-Jul- 15- 15-Jul- 15- 15-Jul- 15- 15-Jul- 15- 15- 15-Jul- 15- 15-Jul- 15- 15-Jul- 15- 15-Jul- 15-
Jan-00 00
00 00 Jan-01 01
01 01 Jan-02 02
02 02 Jan-03 03
03 03 Jan-04 04 Jan-05 05 Jan-06 06 Jan-07 07 Jan-08 08 Jan-09 09 Jan-10 10 Jan-11
04 04 05 05 06 07 08 09 09 10 10 11
Date
Date
11. Spatial distribution of Bio-physical and Household information
Bio-Physical parameters by RS data Household information by surveys
• Land use maps • Crop intensity and Production
• Stress prone areas (drought/submergence) • Income
• Land use changes • Expenditure
• Agro-ecological zones • Savings
• Rainfall (TRMM) • Technology adoption and information