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