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Geospatial tools for Policy and Planning

Leo Kris Palao
Leo Kris Palao

Use and applications of geospatial tools for targeting and prioritisation of interventions and actions.

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Geo-Spatial Tools for Targeting and Prioritisation - Leo Kris M. Palao - International Center for Tropical Agriculture 13 October 2018, 9-11AM CPAF, UPLB
Operational presence in 56 countries across 3 regions 990+ scientists and support staff; 100M+ annual budget 15 intl centers operating in 98 countries through a combined team of 10 thousand++ scientific staff
Agrobiodiversity Soils & Landscapes Decision & Policy Analysis CIAT Research: commodities, systems & futures Bean Tropical Forages Cassava Rice Genetic Resources Climate Change Linking Farmers to Markets Ecosystem Services Sustainable Intensification Land Degradation Climate Smart Agriculture CLIMATE CHANGE for Agriculture & Food Security (CCAFS) and BIG DATA Platform
Overview of spatial analysis Example applications of GIS Tools | Methods Hands-on Exercise | Demo | First map Outline:
Source: Globcover, 2009. ESA 2010 and UCLouvain Important Questions? WHERE? WHEN? HOW? WHAT? Globally, agriculture covers ~37% (48.6M) of all land areas (Worldbank, 2015) Spatial analysis to aid policy and decision making
Environmental Phenomena are dynamic across space and time Source: Baseline: Worldclim (Hijmans, R.J., S.E. Cameron, J.L. Parra, P.G. Jones and A. Jarvis, 2005. Very high resolution interpolated climate surfaces for global land areas. International Journal of Climatology 25:1965-1978) Future: CIAT (International Center for Tropical Agricuture) and Future Earth. Spatial Downscaling Methods: CCAFS-Climate Data Portal. Available online: https://ccafs.cgiar.org/spatial- downscaling-methods DEM SRTM 30m Precipitation (Annual Prec.) Topography Access to water Environmental and production challenges are dynamic in space and time climate, topography, access to water (irrigation) New problems and opportunities are continually emerging- climate change, demography, market, etc. Timely and accurate agricultural information is essential for efficient planning and decision making at local and national scales
With geospatial analysis: Identification of potentials and challenges that can help in efficient targeting and prioritisation Enables to view large area (pattern across space), repeated observation (daily, weekly, monthly reveal pattern with time) Spectral information that human eyes cannot see, can be seen , measured, and quantitatively analyzed
False Color Composite Natural Color Composite Amplify information that are hard to see by the naked eye
Reflectance NDVI [Normalized Difference Vegetation Index] Lifted from Dr. Kyu-Sung Lee presentation. Year 2014 The amount of energy reflected by an object (received by the sensor). Different object/target reflect or absorb suns radiation in different ways Material, physical (growth) , chemical state (moisture), surface roughness Vegetation Soil or bare ground Cloud
Data taken at different temporal intervals can reveal patterns that are important in detecting changes: Phenology, farmer practices, yield, stresses, anomaly Doy 153 Doy 161 Doy 169 Doy 177 Doy 185
Applications / Examples
Near-real time pan-tropical monitoring system for vegetation loss detection Anomalydetection
Geospatial tools for Policy and Planning
2017.03.222017.01.09
Using time series analysis to map crops Red pixels are crop/rice areas
Dry Season 0 0.1 0.2 0.3 0.4 0.5 0.6 001 033 065 097 129 161 193 225 257 289 321 353 VegetationIndex DOY Irrigated double crop rice Wet Season 0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 001 033 065 097 129 161 193 225 257 289 321 353 Vegetationindex DOY Start of SeasonEnd of Season Rainfed Single Crop Flood in monsoon Rice in dry season Detection of cropping patterns/systems Spatio-temporal analysis can be used to detect changes in phenology and stress (plant growth, low rainfall -- drought, high rainfall -- flood) Identify characteristics of each agricultural productions units
Source: International Rice Research Institute. Rice cropping pattern in Myanmar, year 2015. Published but retracted Identify characteristics of each agricultural productions units
NDVI*10000 Year Year 1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .Year16 ANOMALIES Identify stresses that affects agricultural production
Stress Mapping (Flood) Time Backscatter Established crop phenology Phenology interrupted by flood
// Filter by date yyyy-mm-dd var before = collection.filterDate('2015-09-01', '2015-09-30').mosaic(); // Filter by date yyyy-mm-dd var after = collection.filterDate('2015-10-01', '2015-10-30').mosaic();
http://newsinfo.inquirer.net/733370/look-lando-submerges-ricefields-in-pangasinan-and-tarlac-towns
Year Use data to detect unusual patterns (stress periods) Drought from Jan. to Mar. 2016 SOCCSKSARGEN Typical sowing starts at December from observed rice fields
Used for drought assessment in the Philippines for 2015, 2016 Accuracy Drought Presence (~85%), Drought Absence (~>90%) Source: IRRI drought assessment in Mindanao
Spatial modeling to assess risk of climate change to crops
Exposure 1: Sensitivity Climate-Risk Vulnerability Exposure 2: Hazards Pot. Impact Adaptive Capacity Presence of an effect of climate change Characteristics that defines different responses to effects of climate change Management Climate-Risk Vulnerability Assessment to identify priority areas for interventions
Geospatial tools for Policy and Planning

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Geospatial tools for Policy and Planning

  • 1. Geo-Spatial Tools for Targeting and Prioritisation - Leo Kris M. Palao - International Center for Tropical Agriculture 13 October 2018, 9-11AM CPAF, UPLB
  • 2. Operational presence in 56 countries across 3 regions 990+ scientists and support staff; 100M+ annual budget 15 intl centers operating in 98 countries through a combined team of 10 thousand++ scientific staff
  • 3. Agrobiodiversity Soils & Landscapes Decision & Policy Analysis CIAT Research: commodities, systems & futures Bean Tropical Forages Cassava Rice Genetic Resources Climate Change Linking Farmers to Markets Ecosystem Services Sustainable Intensification Land Degradation Climate Smart Agriculture CLIMATE CHANGE for Agriculture & Food Security (CCAFS) and BIG DATA Platform
  • 4. Overview of spatial analysis Example applications of GIS Tools | Methods Hands-on Exercise | Demo | First map Outline:
  • 5. Source: Globcover, 2009. ESA 2010 and UCLouvain Important Questions? WHERE? WHEN? HOW? WHAT? Globally, agriculture covers ~37% (48.6M) of all land areas (Worldbank, 2015) Spatial analysis to aid policy and decision making
  • 6. Environmental Phenomena are dynamic across space and time Source: Baseline: Worldclim (Hijmans, R.J., S.E. Cameron, J.L. Parra, P.G. Jones and A. Jarvis, 2005. Very high resolution interpolated climate surfaces for global land areas. International Journal of Climatology 25:1965-1978) Future: CIAT (International Center for Tropical Agricuture) and Future Earth. Spatial Downscaling Methods: CCAFS-Climate Data Portal. Available online: https://ccafs.cgiar.org/spatial- downscaling-methods DEM SRTM 30m Precipitation (Annual Prec.) Topography Access to water Environmental and production challenges are dynamic in space and time climate, topography, access to water (irrigation) New problems and opportunities are continually emerging- climate change, demography, market, etc. Timely and accurate agricultural information is essential for efficient planning and decision making at local and national scales
  • 7. With geospatial analysis: Identification of potentials and challenges that can help in efficient targeting and prioritisation Enables to view large area (pattern across space), repeated observation (daily, weekly, monthly reveal pattern with time) Spectral information that human eyes cannot see, can be seen , measured, and quantitatively analyzed
  • 8. False Color Composite Natural Color Composite Amplify information that are hard to see by the naked eye
  • 9. Reflectance NDVI [Normalized Difference Vegetation Index] Lifted from Dr. Kyu-Sung Lee presentation. Year 2014 The amount of energy reflected by an object (received by the sensor). Different object/target reflect or absorb suns radiation in different ways Material, physical (growth) , chemical state (moisture), surface roughness Vegetation Soil or bare ground Cloud
  • 10. Data taken at different temporal intervals can reveal patterns that are important in detecting changes: Phenology, farmer practices, yield, stresses, anomaly Doy 153 Doy 161 Doy 169 Doy 177 Doy 185
  • 12. Near-real time pan-tropical monitoring system for vegetation loss detection Anomalydetection
  • 15. Using time series analysis to map crops Red pixels are crop/rice areas
  • 16. Dry Season 0 0.1 0.2 0.3 0.4 0.5 0.6 001 033 065 097 129 161 193 225 257 289 321 353 VegetationIndex DOY Irrigated double crop rice Wet Season 0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 001 033 065 097 129 161 193 225 257 289 321 353 Vegetationindex DOY Start of SeasonEnd of Season Rainfed Single Crop Flood in monsoon Rice in dry season Detection of cropping patterns/systems Spatio-temporal analysis can be used to detect changes in phenology and stress (plant growth, low rainfall -- drought, high rainfall -- flood) Identify characteristics of each agricultural productions units
  • 17. Source: International Rice Research Institute. Rice cropping pattern in Myanmar, year 2015. Published but retracted Identify characteristics of each agricultural productions units
  • 18. NDVI*10000 Year Year 1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .Year16 ANOMALIES Identify stresses that affects agricultural production
  • 19. Stress Mapping (Flood) Time Backscatter Established crop phenology Phenology interrupted by flood
  • 20. // Filter by date yyyy-mm-dd var before = collection.filterDate('2015-09-01', '2015-09-30').mosaic(); // Filter by date yyyy-mm-dd var after = collection.filterDate('2015-10-01', '2015-10-30').mosaic();
  • 22. Year Use data to detect unusual patterns (stress periods) Drought from Jan. to Mar. 2016 SOCCSKSARGEN Typical sowing starts at December from observed rice fields
  • 23. Used for drought assessment in the Philippines for 2015, 2016 Accuracy Drought Presence (~85%), Drought Absence (~>90%) Source: IRRI drought assessment in Mindanao
  • 24. Spatial modeling to assess risk of climate change to crops
  • 25. Exposure 1: Sensitivity Climate-Risk Vulnerability Exposure 2: Hazards Pot. Impact Adaptive Capacity Presence of an effect of climate change Characteristics that defines different responses to effects of climate change Management Climate-Risk Vulnerability Assessment to identify priority areas for interventions