remote sensing products in support of crop subsidy in mexico
DESCRIPTION
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)TRANSCRIPT
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• Two projects are presented
• Retrieve useful information in support of crop
subsidy and/or insurance using RS techniques
• 1. Evaluation of sowing conditions
• 2. Agricultural Drought Index
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Evaluation of
sowing conditions
• PROCAMPO Productivo:
– SAGARPA’s program that subsidies
agricultural activities.
– Eligible areas: those planted as accorded
between producer – program (ha. per field).
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Evaluation of
sowing conditions
• Traditional way of verifying sowing conditions:
– On-field work.
– Cost: $MXN p/year
– ~ 20,000 fields verified.
…a lot
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Evaluation of
sowing conditions
• Using satellite images and advanced remote sensing
techniques, this process can be optimized: considerable
increase in the number of verified fields.
• Classification:
– Object based
– Decision trees
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Evaluation of
sowing conditions
23 CADERs (partials) in 15
states.
Spring-summer season.
Imagery cover
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Imagery
schedule
Image
Pre-processing
Atmospheric correction
Image segmentation
Object aggregation
Sampling
design
Image
useful?
Unsupervised classification
Sampling points selection
Data gathering: planted/non-planted
Auxiliar info: croptype, phenology, % cover, crop height, pictures
6 info layers:
-4 SPOT bands
-NDVI
-Texture (std. dev.)
Results
Y
N
On-field
sampling
Decision trees
(classification)
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Object-based
Segmentation
Layer
aggregation
into objects
4 spectral bands NDVI Texture
= 6 object-based data layers
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Classes
(unsupervised)
Sampling points
Canatlán, Dgo. Sep 2013.
Sampling design
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González, Tamps. Sep. 2013.
On-field sampling
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Sampled points:
N:96
Y: 4
0.96
confidence
LOW
probability
sowed
Sampled points:
N: 11
Y:117
0.91
confidence
HIGH
probability
sowed
Sampled points:
N:20
Y:15
0.57
confidence
UNCERTAIN!
Decision trees
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Miguel Auza, Zac. Aug. 2013.
Classification
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Folio 702901605-1
HIGH PROB. 81%
UNCERTAIN 0%
LOW PROB. 19%
Area cuantification
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State CADER Verified Fields
Zacatecas Miguel Auza 19,409
San Luis Potosí Villa De Ramos 26,715
Guerrero Acapulco 5,860
Durango Canatlán 2,363
Chihuahua El Terrero 2,733
Tamaulipas González 4,793
Puebla Libres 36,520
Tlaxcala Huamantla-Cuapiaxtla 30,198
Oaxaca Pinotepa 5,279
Chihuahua Anahuac, Cusihuiriachi 3,350
Michoacán Venustiano Carranza 4,708
Jalisco La Barca, Ocotlán, Atotonilco El Alto 8,332
Jalisco Lagos de Moreno, Teocaltiche 6,364
Aguascalientes Aguascalientes 2,476
Sinaloa Mazatlán 4,034
Coahuila Monclova, San Buenaventura 1,020
Colima Armería 4,348
15 23 168,502in 4 months
Results
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Agricultural Drought
Index
• Some effects:– Lower yields
– Late planting season
– Early harvest
– Crop re-conversion
– Interruption in cycle
– $$$
• Developed with assessment of National Drought
Mitigation Center (UNL; USA) & Servicio Meteorológico
Nacional (MX).
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Agricultural Drought
Index
• Conditions:
• 1. Rain-fed agriculture
• 2. Monthly delivered
• 3. 1km2 resolution
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Agricultural Drought
Index
• 4. National: in-season areas
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Agricultural Drought
Index
• 4. National: in-season areas
Feb Apr Jun
Aug Oct Dec
Rain-fed agr. (out-of-season)
In-season
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• 1. SPI (anomaly in precipitation)
Variables
+400 stations (MX + US Border) Parameter tuning (for interpolations)
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• 1. SPI (anomaly in precipitation)
Variables
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• 2. VCI (anomaly in NDVI)
Variables
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• 3. TCI (anomaly in LST)
Variables
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• 4. VHI (VCI & TCI)
Variables
Ensenada, BC
Camargo, CHIH
Silao, GTO
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• 4. VHI (VCI & TCI)
Variables
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Variables
integration
0 25 50 75 100
0
0.5
1
Valores originales (VHI) [%]
Valo
res fuzzy
-2 -1 0 1 2
0
0.5
1
Valores originales (SPI)
Valo
res fuzzy
SPI + VHI normalization
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Variables
integration
Drought level
Low
Med
High
Very high
Extreme
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Results
August 2011 August 2013
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Results