enhancing the agriculture and fisheries’ disaster damages and losses assessment using it*...
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Enhancing the Agriculture and Fisheries’Disaster Damages and Losses Assessment
using IT**Presented by Xerxees R. Remorozo, Geo-spatial Information Systems Analyst at the Training Course on the Application of Remote Sensing and GIS Technology in Crop Production.
Beijing, P.R. China. August 27-30, 2013.
• Geo-spatial Information Systems (GIS)– Trend of Damages and Losses through Maps– Post-Disaster Damages Assessment and Field Validation of
Rice Areas in Polanco, Zamboanga del Norte Province: a GIS Approach
• Management Information Systems (MIS)– The Desinventar: Disaster Information Management System (DIMS)
• Remote Sensing (RS)– Conceptual Framework: Real-time Assessment through Satellite
Images of Damages and Losses brought by Weather Disturbances to the Agriculture Sector
• Geo-spatial Information Systems (GIS)– Trend of Damages and Losses through Maps– Post-Disaster Damages Assessment and Field Validation of
Rice Areas in Polanco, Zamboanga del Norte Province: a GIS Approach
• Management Information Systems (MIS)– The Desinventar: Disaster Information Management System (DIMS)
• Remote Sensing (RS)– Conceptual Framework: Real-time Assessment through Satellite
Images of Damages and Losses brought by Weather Disturbances to the Agriculture Sector
OU
TL
INE
OF
P
RE
SE
NTA
TIO
N
Trend of Damages and Lossesthrough Maps
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
NOV
DEC
Agriculture and Fisheries Damages and Losses*(2008-2012)
JAN
* monthly cross-section/ time-elapsed seriesJAN ● FEB ● MAR ● APR ● MAY ● JUN ● JUL ● AUG
SEP ● OCT ● NOV ● DEC ● TOTAL ● RANKING
JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC
50,000,000,000
45,000,000,000
40,000,000,000
35,000,000,000
30,000,000,000
25,000,000,000
20,000,000,000
15,000,000,000
10,000,000,000
5,000,000,000
0
Val
ue
(P)
Months
Calamity
• Flooding• Continuous rains
MostAffected
LeastAffected
Legend:
Source: DA-MID (FOS), 2013
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
NOV
DEC
Agriculture and Fisheries Damages and Losses*(2008-2012)
JAN
* monthly cross-section/ time-elapsed series
50,000,000,000
45,000,000,000
40,000,000,000
35,000,000,000
30,000,000,000
25,000,000,000
20,000,000,000
15,000,000,000
10,000,000,000
5,000,000,000
0
Val
ue
(P)
Months
Calamity
• El Niño• Drought• Earthquake
JAN FEB APR JUN JUL SEP OCT NOV DECMAR MAY AUG
JAN ● FEB ● MAR ● APR ● MAY ● JUN ● JUL ● AUG SEP ● OCT ● NOV ● DEC ● TOTAL ● RANKING
MostAffected
LeastAffected
Legend:
Source: DA-MID (FOS), 2013
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
NOV
DEC
Agriculture and Fisheries Damages and Losses*(2008-2012)
JAN
* monthly cross-section/ time-elapsed series
50,000,000,000
45,000,000,000
40,000,000,000
35,000,000,000
30,000,000,000
25,000,000,000
20,000,000,000
15,000,000,000
10,000,000,000
5,000,000,000
0
Val
ue
(P)
Months
Calamity
• Flooding• Whirlwind/ tornado
JAN FEB APR JUN JUL SEP OCT NOV DECMAR MAY AUG
JAN ● FEB ● MAR ● APR ● MAY ● JUN ● JUL ● AUG SEP ● OCT ● NOV ● DEC ● TOTAL ● RANKING
MostAffected
LeastAffected
Legend:
Source: DA-MID (FOS), 2013
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
NOV
DEC
Agriculture and Fisheries Damages and Losses*(2008-2012)
JAN
* monthly cross-section/ time-elapsed series
50,000,000,000
45,000,000,000
40,000,000,000
35,000,000,000
30,000,000,000
25,000,000,000
20,000,000,000
15,000,000,000
10,000,000,000
5,000,000,000
0
Val
ue
(P)
Months
Calamity
• Typhoons (“Crising” and “Dante”)
JAN FEB APR JUN JUL SEP OCT NOV DECMAR MAY AUG
JAN ● FEB ● MAR ● APR ● MAY ● JUN ● JUL ● AUG SEP ● OCT ● NOV ● DEC ● TOTAL ● RANKING
MostAffected
LeastAffected
Legend:
Source: DA-MID (FOS), 2013
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
NOV
DEC
Agriculture and Fisheries Damages and Losses*(2008-2012)
JAN
* monthly cross-section/ time-elapsed series
50,000,000,000
45,000,000,000
40,000,000,000
35,000,000,000
30,000,000,000
25,000,000,000
20,000,000,000
15,000,000,000
10,000,000,000
5,000,000,000
0
Val
ue
(P)
Months
Calamity
• Tropical Storms (“Cosme”, “Emong”, and “Bebeng”)
• Flooding
JAN FEB APR JUN JUL SEP OCT NOV DECMAR MAY AUG
JAN ● FEB ● MAR ● APR ● MAY ● JUN ● JUL ● AUG SEP ● OCT ● NOV ● DEC ● TOTAL ● RANKING
MostAffected
LeastAffected
Legend:
Source: DA-MID (FOS), 2013
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
NOV
DEC
Agriculture and Fisheries Damages and Losses*(2008-2012)
JAN
* monthly cross-section/ time-elapsed series
50,000,000,000
45,000,000,000
40,000,000,000
35,000,000,000
30,000,000,000
25,000,000,000
20,000,000,000
15,000,000,000
10,000,000,000
5,000,000,000
0
Val
ue
(P)
Months
Calamity
• Typhoons (“Frank”, “Feria”, “Egay”, “Falcon” and “Dindo”)
• Flooding
JAN FEB APR JUN JUL SEP OCT NOV DECMAR MAY AUG
JAN ● FEB ● MAR ● APR ● MAY ● JUN ● JUL ● AUG SEP ● OCT ● NOV ● DEC ● TOTAL ● RANKING
MostAffected
LeastAffected
Legend:
Source: DA-MID (FOS), 2013
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
NOV
DEC
Agriculture and Fisheries Damages and Losses*(2008-2012)
JAN
* monthly cross-section/ time-elapsed series
50,000,000,000
45,000,000,000
40,000,000,000
35,000,000,000
30,000,000,000
25,000,000,000
20,000,000,000
15,000,000,000
10,000,000,000
5,000,000,000
0
Val
ue
(P)
Months
JAN FEB APR JUN JUL SEP OCT NOV DEC
Calamity
• Super Typhoon (“Juaning”)
• Typhoons (“Helen”, “Igme”, “Gorio”, “Isang”, “Basyang” and “Caloy”, “Ferdie” and “Gener”)
MAR MAY AUG
JAN ● FEB ● MAR ● APR ● MAY ● JUN ● JUL ● AUG SEP ● OCT ● NOV ● DEC ● TOTAL ● RANKING
MostAffected
LeastAffected
Legend:
Source: DA-MID (FOS), 2013
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
NOV
DEC
Agriculture and Fisheries Damages and Losses*(2008-2012)
JAN
* monthly cross-section/ time-elapsed series
50,000,000,000
45,000,000,000
40,000,000,000
35,000,000,000
30,000,000,000
25,000,000,000
20,000,000,000
15,000,000,000
10,000,000,000
5,000,000,000
0
Val
ue
(P)
Months
JAN FEB APR JUN JUL SEP OCT NOV DEC
Calamity
• Typhoons (“Karen”, “Nina”, “Kiko”, “Mina”, “Pedring and Quiel”)
• Tropical Storm (“Julian”)
• Mindanao conflict
MAR MAY AUG
JAN ● FEB ● MAR ● APR ● MAY ● JUN ● JUL ● AUG SEP ● OCT ● NOV ● DEC ● TOTAL ● RANKING
MostAffected
LeastAffected
Legend:
Source: DA-MID (FOS), 2013
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
NOV
DEC
Agriculture and Fisheries Damages and Losses*(2008-2012)
JAN
* monthly cross-section/ time-elapsed series
50,000,000,000
45,000,000,000
40,000,000,000
35,000,000,000
30,000,000,000
25,000,000,000
20,000,000,000
15,000,000,000
10,000,000,000
5,000,000,000
0
Val
ue
(P)
Months
JAN FEB APR JUN JUL SEP OCT NOV DEC
Calamity
• Typhoon (“Pepeng”)
• Tropical Storm (“Ondoy”, “Pedring and Quiel”)
MAR MAY AUG
JAN ● FEB ● MAR ● APR ● MAY ● JUN ● JUL ● AUG SEP ● OCT ● NOV ● DEC ● TOTAL ● RANKING
MostAffected
LeastAffected
Legend:
Source: DA-MID (FOS), 2013
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
NOV
DEC
Agriculture and Fisheries Damages and Losses*(2008-2012)
JAN
* monthly cross-section/ time-elapsed series
50,000,000,000
45,000,000,000
40,000,000,000
35,000,000,000
30,000,000,000
25,000,000,000
20,000,000,000
15,000,000,000
10,000,000,000
5,000,000,000
0
Val
ue
(P)
Months
JAN FEB APR JUN JUL SEP OCT NOV DEC
Calamity
• Super Typhoon (“Juan”)
• Typhoon (“Ofel” and “Santi”)
MAR MAY AUG
JAN ● FEB ● MAR ● APR ● MAY ● JUN ● JUL ● AUG SEP ● OCT ● NOV ● DEC ● TOTAL ● RANKING
MostAffected
LeastAffected
Legend:
Source: DA-MID (FOS), 2013
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
NOV
DEC
Agriculture and Fisheries Damages and Losses*(2008-2012)
JAN
* monthly cross-section/ time-elapsed series
50,000,000,000
45,000,000,000
40,000,000,000
35,000,000,000
30,000,000,000
25,000,000,000
20,000,000,000
15,000,000,000
10,000,000,000
5,000,000,000
0
Val
ue
(P)
Months
JAN FEB APR JUN JUL SEP OCT NOV DEC
Calamity
• Monsoon Rains• Flooding
MAR MAY AUG
JAN ● FEB ● MAR ● APR ● MAY ● JUN ● JUL ● AUG SEP ● OCT ● NOV ● DEC ● TOTAL ● RANKING
MostAffected
LeastAffected
Legend:
Source: DA-MID (FOS), 2013
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
NOV
DEC
Agriculture and Fisheries Damages and Losses*(2008-2012)
JAN
* monthly cross-section/ time-elapsed series
Source: DA-MID (FOS), 2013
50,000,000,000
45,000,000,000
40,000,000,000
35,000,000,000
30,000,000,000
25,000,000,000
20,000,000,000
15,000,000,000
10,000,000,000
5,000,000,000
0
Val
ue
(P)
Months
JAN FEB APR JUN JUL SEP OCT NOV DEC
Calamity
• Typhoon (Pablo)• Tropical storms
(“Sendong” and “Quinta”)• Volcanic Eruption• Pest and Diseases
(Rat infestation)
MAR MAY AUG
JAN ● FEB ● MAR ● APR ● MAY ● JUN ● JUL ● AUG SEP ● OCT ● NOV ● DEC ● TOTAL ● RANKING
MostAffected
LeastAffected
Legend:
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
NOV
DEC
Agriculture and Fisheries Damages and Losses*(2008-2012)
JAN
* monthly cross-section/ time-elapsed series
JAN FEB APR JUN JUL SEP OCT NOV DEC
50,000,000,000
45,000,000,000
40,000,000,000
35,000,000,000
30,000,000,000
25,000,000,000
20,000,000,000
15,000,000,000
10,000,000,000
5,000,000,000
0
Val
ue
(P)
Months
Rice 48%
Banana 16%
Corn 12%
Irrigation 8%
HVC 7%Fisheries 5%
Facilities/ Equipment 2%
Coconut 2%
Livestock/ Poultry 1%
P 136 B (5 years)P 27 B (Annual ave.)
MAR MAY AUG
JAN ● FEB ● MAR ● APR ● MAY ● JUN ● JUL ● AUG SEP ● OCT ● NOV ● DEC ● TOTAL ● RANKING
MostAffected
LeastAffected
Legend:
Source: DA-MID (FOS), 2013
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
NOV
DEC
Agriculture and Fisheries Damages and Losses*(2008-2012)
JAN
* monthly cross-section/ time-elapsed series
50,000,000,000
45,000,000,000
40,000,000,000
35,000,000,000
30,000,000,000
25,000,000,000
20,000,000,000
15,000,000,000
10,000,000,000
5,000,000,000
0
Val
ue
(P)
Months
Typhoon
El Nino
Tropical Storm
Super Typhoon
Flooding
Continuous Rains
Moisture Stress
Conflict areas (Mindanao)
Earthquake
Tropical Depression
Whirlwind
Drought
Monsoon Rains
Pest and Diseases
Volcanic Eruption
- 50,000,000,000 100,000,000,000
JAN FEB APR JUN JUL SEP OCT NOV DECMAR MAY AUG
JAN ● FEB ● MAR ● APR ● MAY ● JUN ● JUL ● AUG SEP ● OCT ● NOV ● DEC ● TOTAL ● RANKING
Source: DA-MID (FOS), 2013
MostAffected
LeastAffected
Legend:
Province Rank
Com Val 1
Isabela 2
Cagayan 3
Pangasinan 4
Nueva Ecija 5
Davao Norte 6
Pampanga 7
Tarlac 8
Davao Or. 9
Bulacan 10
Province Rank
Cam Sur 11
Iloilo 12
Kalinga 13
Ilocos Norte 14
Albay 15
La Union 16
Apayao 17
Capiz 18
Misamis Or. 19
Ilocos Sur 20
JAN ● FEB ● MAR ● APR ● MAY ● JUN ● JUL ● AUG SEP ● OCT ● NOV ● DEC ● TOTAL ● RANKING
Vulnerability Ranking(2008-2012)
Damages and Losses brought by Calamities to the Agriculture and Fishery Sectors(2008-2012)
Database Source: MID-FOSBasemap Source: DENR-NAMRIAMap Created by: Cocoy Remorozo
Date: January 11, 2013ALL RIGHTS RESERVED
MostAffected
ModeratelyAffected
LeastAffected
Legend:
Source: DA-MID (FOS), 2013
Post-Disaster Damages Assessment and Field Validation of Rice Areas in Polanco, Zamboanga del Norte: a GIS Approach
Polanco, Zamboanga del Norte (3D)
Agro-climatic data(AWS)
Geo-tagging(Rice areas)
River systems (basemap)Digital Elevation Model
(Watershed)
+ + +
The Desinventar: Disaster Information Management System (DIMS)
• User-friendly• Functionality (temporal/
spatial analysis and GIS)• Open-source• Web-based/ wireless updating• Compatibility• Affordability
Conceptual Framework: Real-time Assessment through Satellite Images of Damages and Losses brought by Weather Disturbances to the Agriculture and Fisheries Sector
LIVESTOCK• Pasteur
lands
CROPS• Rice• Corn• HVC
FISHERIES• Fish
(Culture)
SATELLITE IMAGES
NDVI (vegetation
index)(active sensor)RADARSAT
MODIS(passive sensor)
ConceptualFramework
Real-time Assessment through Satellite Images of Damages and
Losses brought by Weather Disturbances to the Agriculture Sector
Assumptions: •Matrices for Growth stages
•Cost Charts•Algorithms and Formulae
Standing Crops and Built-up Areas
IRRIGATION• NIS• CIS
FARM-TO-MARKET ROADS (FMRs)• Road networksOTHER
FACILITIES
GEO-TAGGED + SHAPEFILES + SATT IMAGES
Reports, Graphics and Geo-statistics
INPUTCriteria
ParametersGuidelines
Policies
PROCESSGIS+RS Applications
OUTPUT Choropleth and Thematic/ Spatial Maps/ Satt Images Geo-statistics
ANALYSIS Damages and Losses /Vulne- rability/ Others
RECOM- MENDATIONPolicy/ Adaptation/
MonitoringGIS
RS+
INTERNATIONAL METEOROLOGICAL
AGENCIES
(agro-climatic data)
• Precipitation/ rainfall• Wind velocity• Relative humidity
☻☻
Growth Stage
Vegetative Reproductive Maturing WindVelocity: 101 - 150 KPH Estimated Yield Loss (%)
12 hrs 20 55 25 12 hrs 25 60 30
Wind Velocity: 150 KPH Estimated Yield Loss (%)
12 hrs 40 80 60 12 hrs 50 80 -100 75
• Palay (strong wind, flood, drought)• Corn (strong wind, flood, drought) • Coconut, abaca, other crops• Fisheries• Livestock & poultry
• Palay (strong wind, flood, drought)• Corn (strong wind, flood, drought) • Coconut, abaca, other crops• Fisheries• Livestock & poultry
Growth Stage
Tillering Panicle Initiation Flowering Ripening
Days Submergence Estimated Yield Loss (%)
1 – 2 10 15 - 25 10 - 15 0
3 - 4 15 - 20 20 - 45 15 – 25 10 - 15 5 - 6 20 - 30 30 - 80 2 0 – 30 15 - 20
7 30 - 50 50 - 100 30 - 70 15 - 20
Damage matrixes (assumptions)
??
Growth Stage
Vegetative Reproductive Maturing WindVelocity: 101 - 150 KPH Estimated Yield Loss (%)
12 hrs 20 55 25 12 hrs 25 60 30
Wind Velocity: 150 KPH Estimated Yield Loss (%)
12 hrs 40 80 60 12 hrs 50 80 -100 75
??
Growth Stage
Vegetative Reproductive Maturing WindVelocity: 101 - 150 KPH Estimated Yield Loss (%)
12 hrs 20 55 25 12 hrs 25 60 30
Wind Velocity: 150 KPH Estimated Yield Loss (%)
12 hrs 40 80 60 12 hrs 50 80 -100 75
G r o w t h S t a g e
T i l l e r i n g P a n i c l e I n i t i a t i o n F l o w e r i n g R i p e n i n g
D a y s S u b m e r g e n c e E s t i m a t e d Y i e l d L o s s ( % )
1 – 2 1 0 1 5 - 2 5 1 0 - 1 5 0
3 - 4 1 5 - 2 0 2 0 - 4 5 1 5 – 2 5 1 0 - 1 5 5 - 6 2 0 - 3 0 3 0 - 8 0 2 0 – 3 0 1 5 - 2 0
7 3 0 - 5 0 5 0 - 1 0 0 3 0 - 7 0 1 5 - 2 0
G r o w t h S t a g e
T i l l e r i n g P a n i c l e I n i t i a t i o n F l o w e r i n g R i p e n i n g
D a y s S u b m e r g e n c e E s t i m a t e d Y i e l d L o s s ( % )
1 – 2 1 0 1 5 - 2 5 1 0 - 1 5 0
3 - 4 1 5 - 2 0 2 0 - 4 5 1 5 – 2 5 1 0 - 1 5 5 - 6 2 0 - 3 0 3 0 - 8 0 2 0 – 3 0 1 5 - 2 0
7 3 0 - 5 0 5 0 - 1 0 0 3 0 - 7 0 1 5 - 2 0
G r o w t h S t a g e
T i l l e r i n g P a n i c l e I n i t i a t i o n F l o w e r i n g R i p e n i n g
D a y s S u b m e r g e n c e E s t i m a t e d Y i e l d L o s s ( % )
1 – 2 1 0 1 5 - 2 5 1 0 - 1 5 0
3 - 4 1 5 - 2 0 2 0 - 4 5 1 5 – 2 5 1 0 - 1 5 5 - 6 2 0 - 3 0 3 0 - 8 0 2 0 – 3 0 1 5 - 2 0
7 3 0 - 5 0 5 0 - 1 0 0 3 0 - 7 0 1 5 - 2 0
• Use of modern technologies (eg. GIS, MIS and satellite images) in monitoring of damages to improve the accuracy and timeliness of reports
• Generation of maps identifying vulnerable areas for planning and mitigation
• Weather-based insurance schemes for rapid appraisals and claims
• “Change and innovate, or else we will perish...”
• Use of modern technologies (eg. GIS, MIS and satellite images) in monitoring of damages to improve the accuracy and timeliness of reports
• Generation of maps identifying vulnerable areas for planning and mitigation
• Weather-based insurance schemes for rapid appraisals and claims
• “Change and innovate, or else we will perish...”
Con
clus
ion
Thank you…Xerxees R. Remorozo
Geo-spatial Information Systems AnalystRepublic of the Philippines
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