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

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 sun’s 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

11. Applications / Examples

12. Near-real time pan-tropical monitoring system for vegetation loss detection
Anomalydetection

14. 2017.03.222017.01.09

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();

21. http://newsinfo.inquirer.net/733370/look-lando-submerges-ricefields-in-pangasinan-and-tarlac-towns

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