1. 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.

2. 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 OUTLINE OF PRESENTATION

3. Trend of Damages and Losses through Maps

4. 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 JANJAN ● FEB ● MAR ● APR ● MAY ● JUN ● JUL ● AUGFEB MARAPRMAYJUNJUL AUG SEPSEP ● OCT ● NOV ● DEC ● TOTAL ● RANKINGOCTNOVDECTOTALRANKING JANFEBMARAPRMAYJUNJULAUGSEPOCTNOVDEC 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 Value (P) Months Calamity Flooding Flooding Continuous rains Continuous rains Most Affected Least Affected Legend: Source: DA-MID (FOS), 2013

5. 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 Value (P) Months Calamity El Niño El Niño Drought Drought Earthquake Earthquake JANFEBAPRJUNJULSEPOCTNOVDECMARMAYAUG JANJAN ● FEB ● MAR ● APR ● MAY ● JUN ● JUL ● AUGFEB MARAPRMAYJUNJUL AUG SEPSEP ● OCT ● NOV ● DEC ● TOTAL ● RANKINGOCTNOVDECTOTALRANKING Most Affected Least Affected Legend: Source: DA-MID (FOS), 2013

6. 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 Value (P) Months Calamity Flooding Flooding Whirlwind/ tornado Whirlwind/ tornado JANFEBAPRJUNJULSEPOCTNOVDECMARMAYAUG JANJAN ● FEB ● MAR ● APR ● MAY ● JUN ● JUL ● AUGFEB MARAPRMAYJUNJUL AUG SEPSEP ● OCT ● NOV ● DEC ● TOTAL ● RANKINGOCTNOVDECTOTALRANKING Most Affected Least Affected Legend: Source: DA-MID (FOS), 2013

7. 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 Value (P) Months Calamity Typhoons (“Crising” and “Dante”) Typhoons (“Crising” and “Dante”) JANFEBAPRJUNJULSEPOCTNOVDECMARMAYAUG JANJAN ● FEB ● MAR ● APR ● MAY ● JUN ● JUL ● AUGFEB MARAPRMAYJUNJUL AUG SEPSEP ● OCT ● NOV ● DEC ● TOTAL ● RANKINGOCTNOVDECTOTALRANKING Most Affected Least Affected Legend: Source: DA-MID (FOS), 2013

8. 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 Value (P) Months Calamity Tropical Storms (“Cosme”, “Emong”, and “Bebeng”) Tropical Storms (“Cosme”, “Emong”, and “Bebeng”) Flooding Flooding JANFEBAPRJUNJULSEPOCTNOVDECMARMAYAUG JANJAN ● FEB ● MAR ● APR ● MAY ● JUN ● JUL ● AUGFEB MARAPRMAYJUNJUL AUG SEPSEP ● OCT ● NOV ● DEC ● TOTAL ● RANKINGOCTNOVDECTOTALRANKING Most Affected Least Affected Legend: Source: DA-MID (FOS), 2013

9. 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 Value (P) Months Calamity Typhoons (“Frank”, “Feria”, “Egay”, “Falcon” and “Dindo”) Typhoons (“Frank”, “Feria”, “Egay”, “Falcon” and “Dindo”) Flooding Flooding JANFEBAPRJUNJULSEPOCTNOVDECMARMAYAUG JANJAN ● FEB ● MAR ● APR ● MAY ● JUN ● JUL ● AUGFEB MARAPRMAYJUNJUL AUG SEPSEP ● OCT ● NOV ● DEC ● TOTAL ● RANKINGOCTNOVDECTOTALRANKING Most Affected Least Affected Legend: Source: DA-MID (FOS), 2013

10. 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 Value (P) Months JANFEBAPRJUNJULSEPOCTNOVDEC Calamity Super Typhoon (“Juaning”) Super Typhoon (“Juaning”) Typhoons (“Helen”, “Igme”, “Gorio”, “Isang”, “Basyang” and “Caloy”, “Ferdie” and “Gener”) Typhoons (“Helen”, “Igme”, “Gorio”, “Isang”, “Basyang” and “Caloy”, “Ferdie” and “Gener”) MARMAYAUG JANJAN ● FEB ● MAR ● APR ● MAY ● JUN ● JUL ● AUGFEB MARAPRMAYJUNJUL AUG SEPSEP ● OCT ● NOV ● DEC ● TOTAL ● RANKINGOCTNOVDECTOTALRANKING Most Affected Least Affected Legend: Source: DA-MID (FOS), 2013

11. 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 Value (P) Months JANFEBAPRJUNJULSEPOCTNOVDEC Calamity Typhoons (“Karen”, “Nina”, “Kiko”, “Mina”, “Pedring and Quiel”) Typhoons (“Karen”, “Nina”, “Kiko”, “Mina”, “Pedring and Quiel”) Tropical Storm (“Julian”) Tropical Storm (“Julian”) Mindanao conflict Mindanao conflict MARMAYAUG JANJAN ● FEB ● MAR ● APR ● MAY ● JUN ● JUL ● AUGFEB MARAPRMAYJUNJUL AUG SEPSEP ● OCT ● NOV ● DEC ● TOTAL ● RANKINGOCTNOVDECTOTALRANKING Most Affected Least Affected Legend: Source: DA-MID (FOS), 2013

12. 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 Value (P) Months JANFEBAPRJUNJULSEPOCTNOVDEC Calamity Typhoon (“Pepeng”) Typhoon (“Pepeng”) Tropical Storm (“Ondoy”, “Pedring and Quiel”) Tropical Storm (“Ondoy”, “Pedring and Quiel”) MARMAYAUG JANJAN ● FEB ● MAR ● APR ● MAY ● JUN ● JUL ● AUGFEB MARAPRMAYJUNJUL AUG SEPSEP ● OCT ● NOV ● DEC ● TOTAL ● RANKINGOCTNOVDECTOTALRANKING Most Affected Least Affected Legend: Source: DA-MID (FOS), 2013

13. 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 Value (P) Months JANFEBAPRJUNJULSEPOCTNOVDEC Calamity Super Typhoon (“Juan”) Super Typhoon (“Juan”) Typhoon (“Ofel” and “Santi”) Typhoon (“Ofel” and “Santi”) MARMAYAUG JANJAN ● FEB ● MAR ● APR ● MAY ● JUN ● JUL ● AUGFEB MARAPRMAYJUNJUL AUG SEPSEP ● OCT ● NOV ● DEC ● TOTAL ● RANKINGOCTNOVDECTOTALRANKING Most Affected Least Affected Legend: Source: DA-MID (FOS), 2013

14. 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 Value (P) Months JANFEBAPRJUNJULSEPOCTNOVDEC Calamity Monsoon Rains Monsoon Rains Flooding Flooding MARMAYAUG JANJAN ● FEB ● MAR ● APR ● MAY ● JUN ● JUL ● AUGFEB MARAPRMAYJUNJUL AUG SEPSEP ● OCT ● NOV ● DEC ● TOTAL ● RANKINGOCTNOVDECTOTALRANKING Most Affected Least Affected Legend: Source: DA-MID (FOS), 2013

15. 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 Value (P) Months JANFEBAPRJUNJULSEPOCTNOVDEC Calamity Typhoon (Pablo) Typhoon (Pablo) Tropical storms (“Sendong” and “Quinta”) Tropical storms (“Sendong” and “Quinta”) Volcanic Eruption Volcanic Eruption Pest and Diseases (Rat infestation) Pest and Diseases (Rat infestation) MARMAYAUG JANJAN ● FEB ● MAR ● APR ● MAY ● JUN ● JUL ● AUGFEB MARAPRMAYJUNJUL AUG SEPSEP ● OCT ● NOV ● DEC ● TOTAL ● RANKINGOCTNOVDECTOTALRANKING Most Affected Least Affected Legend:

16. 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 JANFEBAPRJUNJULSEPOCTNOVDEC 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 Value (P) Months P 136 B (5 years) P 27 B (Annual ave.) MARMAYAUG JANJAN ● FEB ● MAR ● APR ● MAY ● JUN ● JUL ● AUGFEB MARAPRMAYJUNJUL AUG SEPSEP ● OCT ● NOV ● DEC ● TOTAL ● RANKINGOCTNOVDECTOTALRANKING Most Affected Least Affected Legend: Source: DA-MID (FOS), 2013

17. 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 Value (P) Months JANFEBAPRJUNJULSEPOCTNOVDECMARMAYAUG JANJAN ● FEB ● MAR ● APR ● MAY ● JUN ● JUL ● AUGFEB MARAPRMAYJUNJUL AUG SEPSEP ● OCT ● NOV ● DEC ● TOTAL ● RANKINGOCTNOVDECTOTALRANKING Source: DA-MID (FOS), 2013 Most Affected Least Affected Legend:

18. Province ProvinceRank 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 ProvinceRank 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 JANJAN ● FEB ● MAR ● APR ● MAY ● JUN ● JUL ● AUGFEB MARAPRMAYJUNJUL AUG SEPSEP ● OCT ● NOV ● DEC ● TOTAL ● RANKINGOCTNOVDECTOTALRANKING Vulnerability Ranking (2008-2012) Damages and Losses brought by Calamities to the Agriculture and Fishery Sectors (2008-2012) Database Source: MID-FOS Basemap Source: DENR-NAMRIA Map Created by: Cocoy Remorozo Date: January 11, 2013 ALL RIGHTS RESERVED Most Affected Moderately Affected Least Affected Legend: Source: DA-MID (FOS), 2013

19. Post-Disaster Damages Assessment and Field Validation of Rice Areas in Polanco, Zamboanga del Norte: a GIS Approach

20. Polanco, Zamboanga del Norte (3D) Agro-climatic data (AWS) Geo-tagging (Rice areas) River systems (basemap) Digital Elevation Model (Watershed) + ++

21. The Desinventar : Disaster Information Management System (DIMS)

23. User-friendly User-friendly Functionality (temporal/ spatial analysis and GIS) Functionality (temporal/ spatial analysis and GIS) Open-source Open-source Web-based/ wireless updating Web-based/ wireless updating Compatibility Compatibility Affordability Affordability

24. Conceptual Framework: Real-time Assessment through Satellite Images of Damages and Losses brought by Weather Disturbances to the Agriculture and Fisheries Sector

25. 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 networks OTHER FACILITIES GEO-TAGGED + SHAPEFILES + SATT IMAGES Reports, Graphics and Geo- statistics INTERNATIONAL METEOROLOGICAL AGENCIES (agro-climatic data) Precipitation/ rainfall Wind velocity Relative humidity

26. ☻ ☻ Palay ( strong wind, flood, drought)Palay ( strong wind, flood, drought) Corn (strong wind, flood, drought)Corn (strong wind, flood, drought) Coconut, abaca, other cropsCoconut, abaca, other crops FisheriesFisheries Livestock & poultryLivestock & poultry Palay ( strong wind, flood, drought)Palay ( strong wind, flood, drought) Corn (strong wind, flood, drought)Corn (strong wind, flood, drought) Coconut, abaca, other cropsCoconut, abaca, other crops FisheriesFisheries Livestock & poultryLivestock & poultry Damage matrixes (assumptions)

28. 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...” Conclusion

29. Thank you… Xerxees R. Remorozo Geo-spatial Information Systems Analyst Republic of the Philippines