1. Chandrashekhar Biradar, Abdallah Bari,
Roy, P.S., Prashant Patil, Ahmed Amri,
Murari Sing, and C. Jeganathan
Applied Mathematics and Omics Technologies for Discovering Biodiversity and Genetic Resources for
Climate Change Mitigation and Adaptation to Sustain Agriculture in Drylands. June 24-27, 2014, Morocco, Rabat
International Workshop on

2. River Flow
Ecosystem Energy
Habitat
Delta
Communication
Agriculture
Groundwater
Water use
Flood
Drought
Earth Observation
Horticulture
Weather
Biodiversity

3. CRP Policy, Institutions and Markets
CRP Wheat
CRP Grain Legumes
CRP Dryland Cereals
CRP Livestock and Fish
CRP Water, Land and Ecosystems
CRP Climate Change, Agriculture and Food Security
CRP Gene Banks
CRP Dryland Systems (and Action sites)
ICARDA Led and
Partnership CRPs
Regional Programs & Dryland Systems

4. Gender
Health
Youth &
Capacity Dev.
Address social
inequities,
greater roles and
priorities
Engaging and
empowering young
gen. by creating
opportunities
Changing diet
patterns,
nutrition and
health
$Total 122.7m
Startup 35.47m
156Remote sensing
missions in orbit
Sensors potential in
CRPs/IRPs, etc.
41%
Drylands
Earth‘s
land area
2.5bLive in
Drylands
People
1) The West African Sahel and dry
savannas , 2) East and Southern
Africa , 3) North Africa and West Asia
4) Central Asia , and 5) South Asia.
Regions
FoodSecurity
Livelihoods
Improved
Ensuring
increased
Livestock
5
Depend on
Drylands1.5b
Red. Vul.
Sus. Int.
A/Ss
TAs
A/Ss
TAs
Spatial enrichment
and its role in food
security, risk
mitigation, &
sustainabilityFood production
potential sources
Agricultural
Intensification
Cropping Intensity
Increase in
Arable Land
72%
21%
7%
Dryland
Systems
>12
>6 are free
Biodiversity
Integrated Production
Systems for Improving
Food Security and
Livelihoods in
Dry Areas
Efficiency
Productivity
Role of Geospatial Science,
Technology and Applications
(GeSTA) in Agro-Ecosystems
Integrated agro-
ecosystems: innovative
approaches and
methods for sustainable
agriculture, while
safeguarding the
environment
Cooperative
Research and
Partnerships
Specific
mutual-interaction
& synergies
between plant and
animal species and
management
practices
Quantification
of existing
agricultural
production
systems
Characterization of
vulnerable areas for
increasing resilience and
assist in identifying
mitigation pathways with
biophysical, socioeconomic
and stakeholder feedback as
well as specific needs &
constraints
Mapping present,
emerging & future
land use /land cover
dynamics, cropping
patterns, forage, intensities,
water use, pest & diseases,
climate change &
impacts
Characteristics of
agricultural and
livestock production in
small holder farming
systems and rural
livelihoods
Delineation of
potential, suitable
areas for sustainable
intensification, and
diversification of ag.
Innovation production
systems
Status &
trends of
existing
production
systems Assessment of
present, emerging &
future droughts,
floods, pests &
diseases, extreme
events, infrastructure,
migration
Mapping the extent
of existing & traditional
practices, indigenous
knowledge, diversity,
potential areas for modern
& improved, productive,
profitable, and diversified
dryland agriculture, &
linkages to markets
Assessing the
impact of outcomes
in Action Sites,
post-project
implementation, &
M&E
Measuring the
impact at spatial
scales, rate,
magnitude, synergy
among the systems,
CRPs, cross-regional
synthesis
Geospatial
commons ,
KM sharing,
stakeholder
feedback
Farmers,
stakeholders,
policymakers,
mobilization, &
marketing

5. CRP
DS
CRP
PIM
CRP
CRP
GL
CRP
DC
CRP
L&F
CRP
FTA
CRP
WLE
CRP
CCAFS
GB
Dryland Cereals
Validating high yielding varieties with
better pest and disease resistance,
tolerance to abiotic stresses, and
improved crop management
technologies
Sustainable intensification-
challenges and constraints,
integrated crop and pest
management practices, and
value chain linkages
Grain Legumes
Improving productivity and
profitability of wheat,
improved resistance to
pests and diseases, climate
resilient, and increasing
yield while reducing inputs
Wheat
Livestock and Fish
Resilience and vulnerability of livestock
production under changing climate, land
use and markets, identify and address key
constraints and opportunities
Sustainable intensification, challenges
and constraints, integrated crop and pest
management practices, and value chain
linkages
Policies, Institutions, and Markets
Forest, Trees and Agroforestry
Improving synergies between
agriculture, forest, trees as an
integrated systems to enhance the
sustainable production systems
Water, Land and Ecosystems
Improving land and water,
productivity, and ecosystem
services. Assessment of land
degradation, soil health and
nutrition, and climate
change impact
Climate Change
Eco-friendly climate change
adoption - strengthening
approaches for better
management of agricultural
risks associated with
increased climate variability
and extreme events
Gene Banks
Managing biodiversity in agro-systems.
Focused Identification of Germplasm
Strategy. Characterization of genetic
resources at landscape level.
Bio-physical-spectral
libraries for mapping
agricultural productivity
Mapping inter and intra
variability at species,
field, and farm scales
Hyper and ultra spectral
mapping of genotypic and
phenotypic variability
Geo-referenced in
situ/field photo
and data collection
Innovative tools and techniques
for improved agronomic
practices and management
Quantification of trends
and status of soil fertility,
salinity, and degradation,
Integrated pests and
diseases management
Location based
services in natural
resource management
Enhancing productivity and managing risks
through diversification, sustainable intensification,
and integrated agro-ecosystem approaches
Dryland Systems
Crop
spectra
WHEAT
Crosscutting themes and linkages of CGIAR Research Programs* (CRPs)
*ICARDA
Led/Involved

6. Biological Richness
Human Induced Disturbance
Mittermeter, 1998
Hannah & Lohse, 1993
Sunlight Water
Temperature
Potential Climate Limits
Ever Changing Climate
Genetic Resources in Peril

7. Change in Precipitation
1980/1999 to 2080/2099
Global Scale

8. Change in Temperature
1980/1999 to 2080/2099
Global Scale

9. 19
6
10
16
1
14
17
6
11
22
20
7
19
25
13
8
4
9
5
18
2
24
15
14
12
21
1. Tropical Andes, 2. Sundaland, 3. Mediterranean Basin, 4. Madagascar & Indian Ocean Islands, 5. Indo-Burma, 6. Caribbean, 7. Atlantic Forest Region, 8. Philippines,
9. Cape Floristic Province, 10. Mesoamerica 11. Brazilian Cerrado, 12. Southwest Australia, 13. Mountains of South-Central China14. Polynesia/Micronesia 15. New
Caledonia, 16. Chocó-Darién-Western Ecuador, 17. Guinean Forests of West Africa, 18. Western Ghats & Sri Lanka, 19. California Floristic Province, 20. Succulent Karoo,
21. New Zealand, 22. Central Chile, 23. Caucasus, 24. Wallacea, 25. Eastern Arc Mountains & Costal Forests of Tanzania & Kenya
High Biodiversity and High Disturbance
High Biodiversity and Low Disturbance
Low Biodiversity and High Disturbance
Low Biodiversity and Low Disturbance
Biodiversity Hot Spots of the World
Hotspots

10. MODIS-USGS
1993-2003
MODIS 2001-2002 MODIS 2002-2003
Bareland Forest-BarelandForest
Climate extremes (changes in Rainfall)
and Land use change

11. National to Local Scale
Climate Extremes
Climate
Deviation
from long-
term mean

12. Vulnerability Index
Low High
Vulnerability of crops to Pests & Diseases
Stripe Rust in Wheat

13. Reflectance-Based Characterization of Wheat
Cultivars for Identifying Drought Tolerance

14. Vegetation Dynamics: Extreme Events
2000 to Current
0.00
0.20
0.40
0.60
0.80
1.00
1.20
2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013
EnhancedVegetationIndex(EVI)
8-days interval from 2000-2013
Water deficit years (droughts)Water surplus years (good years)
MODIS Time-Series Spectral Profile for Drylands Vegetation (2000-2013)

15. 1/1/07
3/1/07
5/1/07
7/1/07
9/1/07
11/1/07
1/1/08
FWI
0.0
0.2
0.4
0.6
0.8
1.0
LSWI
-0.4
-0.2
0.0
0.2
0.4
0.6
T° H20 T°
1/1/08
3/1/08
5/1/08
7/1/08
9/1/08
11/1/08
1/1/09
FWI
0.0
0.2
0.4
0.6
0.8
1.0
LSWI
-0.4
-0.2
0.0
0.2
0.4
0.6
T° H20 T°
1/1/09
3/1/09
5/1/09
7/1/09
9/1/09
11/1/09
1/1/10
FWI
0.0
0.2
0.4
0.6
0.8
1.0
LSWI
-0.4
-0.2
0.0
0.2
0.4
0.6
T° H20 T° 1/1/10
3/1/10
5/1/10
7/1/10
9/1/10
11/1/10
1/1/11
FWI
0.0
0.2
0.4
0.6
0.8
1.0
LSWI
-0.4
-0.2
0.0
0.2
0.4
0.6
T° H20 T°
Extreme Climate Events
Growing Season
Drought event
FWI-25
FWI-60
LSWI
Productivity in Response
to Drought Events

16. Vegetation vs. Species Richness

17. 0.00
0.20
0.40
0.60
0.80
1.00
1.20
2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013
EnhancedVegetationIndex(EVI)
8-days interval from 2000-2013
Water deficit years (droughts)Water surplus years (good years)
Climate Change Impact?

18. ARIMA (Autoregressive Integrated Moving Average) model
analysis for the period of 2000 to 2013 to forecast
Climate Change Impact?

19. Mapping, Monitoring, Management,…
Never Ending?
Biodiversity
Evaluation
Biodiversity
Management

20. Landscape Ecology in Biodiversity
Landscape
Ecology
Environment
(Broad Extent)
Disturbance
(Human)
Habitat
(Pattern)
Landscape ecology is the science of studying interactions in the landscapes;
specifically, the composition, structure and function of landscapes.
What’s a
‘landscape’?
Landscape ecology involves
the study of the interactions
between spatial
heterogeneity, broader
extents and role of humans
and how these interactions
change over time.
Application of these
principles and interactions in
the formulation and solving
of real-world problems.
“Study of Landscapes”

21. Landscape Ecology
Composition
and
Dynamics
Function
Structure
• Patch-basic unit of
landscape structure
• Characteristics and
spatial relationship
• Changes in pattern and
process over time
• Characteristics of structure
and function
• Interaction between
structure, composition and
dynamics

22. Today, there is a shift from broad inventory surveys towards the techniques
that can predict species occurrence, habitat types and genetic impacts with
the help of spatial and temporal tools –Remote Sensing (RS), Geographical
Information System (GIS) and Global Positioning Systems (GPS).
Mapping the spatial distribution of landscape elements (e.g., habitat types)
at local to global scale can be done efficiently with the help of temporal
remotely sensed data along with field surveys. Temporal data helps in
accessing rates of transformation of habitat and the threats to different
species as a result of ever changing landscapes.
Such analysis helps in assigning conservation priorities to threatened, rare,
endemic and taxonomically distinct species and to different types of habitats
or landscape elements on the basis of richness and significance of the
threatened species that occur.
Role of Geoinformatics

23. Role of Remote Sensing in Landscape Ecology

24. Geoinformatics
Theory
Spatial Database
Maps & Attributes
Comp. Geometry
Spatial Analysis
Spatial Data Mining
Remote Sensing
Navigation System
Spatial Decisions
Telecommunication
Agro-ecosystems
Environment
Climate Change
Spatial Models
Spatial Algorithms
Spatial Reasoning
Biodiversity
and Crop
Improvement
Land and Water
Resources
Crop and
Livestock
Productions
Socio-
Economics,
Markets and
Policy
Science, Technology
& Applications

25. Recent Advances in Geoinformatics
Multispectral
Hyperspectral
Thermal
Microwave
• Increased Spatial Resolution
• ~50cm, 1m, 2.5m, 4m, 5m…
• Increased Spectral Resolution
• hyperspectral, ultraspectral, thermal, SAR,..
• specific bands for agricultural applications…
• Increased Temporal Resolution
• daily @ higher spatial resolution…
• Increased Computational speed
• high end PCs/WS, cloudC, multithreading…
• Improved Image Processing Techniques
• VMs, Feature Ex, Fusion, KBCs, Decision trees…
• Decreased Cost of the Hardware
• servers and storage systems …
• Deceased Software cost:
• open source programs/OS …
• Decreased RS Data cost
• free and open access data sharing…

26. Satellite Resolution(m)* Pixels/ac Pixels/ha $/km2
AVHRR 1000 0.004 0.01 Free
SPOT 1000 0.004 0.01 Free
MODIS 500 0.016 0.04 Free
Landsat 30 4.5 11.1 Free
PALSAR 10 40 100 Free
AWiFS 60 1.11 2.7 0.01
IRS liss3 23.5 7.3 18.1 0.15
ASTER 15 18 44.4 0.04
IRS Liss4 5 160 400 1.19
RapidEye 5 160 400 1.23
IKONOS 4 253 625 5.02
Cartosat1 2.5 640 1600 6.59
GeoEye1 2 1012 2500 12.5
WorldView2 2 1012 2500 14.5
PLEIADES 2 1012 2500 17
Evolving pixels to specific needs!
Global to Local Scale
* Multispectral

27. Platforms
Mode Hyperspectral Multispectral Optical LiDAR LiDAR SAR
Sensor ASD FieldSpec M× Camera APs/UAVs Lidar WorldView-2 Landsat
ETM+
MODIS ICESat* PALSAR
Spectral
resolution
350-2500nm 4 bands 3-4 bands 1264nm 8 bands 7 bands 7/36 bands* 1264 & 532nm L band
Spatial resolution 0.1-1.5m 0.1-0.2m 1-m 20 - 80cm 0.46m Pan;
1.84m MS
15m Pan;
30m MS
250m, 500m,
1000m MS
70m 10m, 20m,
100m
Swath 1-4m 2-10m -- 1-2km 16.4km 185km 2330km 35-250km
Revisit -- -- 3-year -- 1.1 days 16 days 1 day 91 days 46 days
Plant biomass × × × × × × ×
Plant height × × ×
LAI, fPAR, LST × × × × ×
NDVI, EVI, LSWI × × × × × ×
Erosion, Salinity × × × × × × ×
Soil moisture × × × × × ×
Chlolophyll × × × × x ×
Nitrogen × × × × ×
Phosphorous × × ×
Plant water × × × ×
GPP × × × × ×
NPP × × × ×
land cover/use × × × × × × ×
phenology × × x × ×
Irrigation × × × × × × ×
DEM × × × × × ×
Derivatives × × × × ×
Tier 1 AOIs × × × × × × × × ×
Tier 2 action sites × × × × × × ×
Tier 3 AEZs × × × × × ×
Tier 4 Target × × ×
BiochemicalBiophysical
Produc
tion
LULCTerrainScale
RSdata
characteristics
Ground/in-situ Airborne Spaceborne
Optical
Remote Sensing Matrix
Example of One Sensor in each Platform
Characteristics for Quantification

28. Earth Observation Systems for Agro-Ecosystem Research
ACTIVE SATELLITE SENSORS AND CHARACTERSTICS
Very High Resolution ( Up to - 1 m)
High Resolution ( 1 to 5 m)
Radar Satellites
Medium resolution (5 - 30 m)
Low or Medium
resolution
Satellite Bands Band (Polarity)
Swath width
(km)
Sentinel-1
COSMO-SKYMED 4 1, 5, 15, 30, 100 X-B (HH, VV, HV, VH)
10, 40, 30, 100,
200
TanDEM-X 1, 3, 16 X-B (HH, VV, HV, VH) 1500
COSMO_SKYMED 2 1, 5, 15, 30, 100 X-B (HH, VV, HV, VH)
10, 40, 30, 100,
200
RADARSAT 2
3, 8, 12, 18, 25, 30, 40, 50,
100 C-B (HH, HV, VH, VV) 5 - 500
COSMO-SKYMED 1 1, 5, 15, 30, 100 X-B (HH, VV, HV, VH)
10, 40, 30, 100,
200
Terra SAR-X 1, 3, 16 X-B (HH, VV, HV, VH) 1500
ALOS (PALSAR) 10, 20, 30, 100
L-B (HH, VV, HH, HV,
VH) 70
ENVISAT (ASAR) 12.5 C-B (VV) 5 - 406
RADARSAT 1 (SAR) 8,25, 30, 35, 50, 100 C-B (HH) 50 - 500
ERS 2 (AMI) 25 C-B (VV) 100
ERS 1 (AMI) 25 C-B (VV) 100
Satellite Multispectral resolution (m)B, s
Swath width
(km)
Landsat 8 30 (14.8)
P, C, B, G, R, IR, SW
(3)
185
VIIRS 375, 750 22b, s 3000
ASAR (12.5) VV 1 5 - 406
MERIS 300 15 b, s 1150
Metosat MSG
GERB 40000 7 -
SEVIRI 1000, 3000 12 -
SPOT5/VEGETATION 2 1000 B, R, IR, SW (4) 2250
MODIS 250, 500, 1000 36 2330
SPOT4/VEGETATION 1 1000 B, R, IR, SW (4) 60
IRS-1D/ WiFS 188 R, IR (2) 774
Orbview-2/ SeaWiFS 1130 B(2), G (3), IR (8) 2800
IRS-1C/ WiFS 188 R, IR (2) 810
RESURS-01-1/ MSU-S 240 G, R, IR (3) 600
RESURS-01-1/ MSU-SK 170, 600 R, G, IR(2), TIR 600
ResourceSat/AWiFS 56 R, G, IR, SW 740
Landsat 2/ MSS 80 G, R, IR, IR 183
Landsat 2/ RBV 80 G, R, IR 183
Landsat 1/ MSS 80 G, R, IR, IR 183
Landsat 1/ RBV 80 G, R, IR 183
Satellite Multispectral resolution (m) B, s Swath width (km)
ASTER (15m)
VNIR (Visible Near Infrared) 15 VIR (4) 60
SWIR (Shortwave Infrared) 30 SW (6) 60
TIR (Thermal Infrared) 60 TIR (5) 60
CBERS - 2
WFI 260 R, IR 890
CCD 20 B, G, R, IR 113
IRMSS (2.7) P- 27
LANDSAT 5TM -7ETM 30 (14.8)
B, G, R, IR, SW1, TIR, SW2,
P
185
Nigeriasat-X 22 G, R, IR -
Resourcesat-2/Liss-III 23.5 R, G, IR, SW 141
Deimos-1 22 G, R, IR 600
UK-DMC-2/SLIM6 22 G, R, IR 638
BILSAT-1 26 (12) R, B, G, IR, P 640
Nigeriasat-1 32 G, R, IR 640
ALSAT-1 32 G, R, IR 640
UK-DMC/EC (DMC) 32 G, R, IR 600
EO-1/ALI-MS 30 B (2), G, R, IR (3), SW (2), P 37
EO-1/ Hyperion 30 220 bands 7,7
ASTER (15m) 15, 30, 90 G, R, IR (2) SW(6), TIR (4) 60
LANDSAT 7ETM+ 30m (14.5) B, G, R, IR, SW (2), TIR, P 185
SPOT-4 20 (10) G, R, IR, SW, P 60
SPOT-3 20 (10) G, R, IR+P 60
JERS-1 24 (18) G, R, IR, IR 75
SPOT-2 20 (10) G, R, IR 60
SPOT-1 20 (10) G, R, IR 60
Landsat 5/MSS 80 G, R, IR, IR 185
Landsat 5/TM 30, 120 B, G, R, IR, SW, SW, TIR 185
RESURS-01-1 45 G, R, IR 600
*=Resolution in parenthesis is panchromatic
+=Bands: B-Blue, G-Green, R-Red, IR-Infra Red, C-Coastal blue, Y-Yellow, SW-Shortwave Infrared, M-Mid infrared, P-Panchromatic, H-Horizonal, V-vertcial
Satellite
Sensors
Resolution
Swath (km)
Spatial (m)* Temporal (days) Spectral (Bands)
GEOEYE-1 1.65 (0.41) 1 B, G, R, IR, P 15.2
IKONOS 3.2 (0.82) 14 B, G, R, IR, P 11.3
PLEIADES-1A 2 (0.5) 1 B, G, R, IR, P 20
PLEIADES-1B 3 (0.5) 1 B, G, R, IR, P 20
Quick Bird 2.4 (0.6) 3.5 B, G, R, IR, P 16.5
WorldView-1 (0.4) 1.2 P 17.6
WorldView-2 1.8 (0.4) 1.2
P, C, B, G, Y, R, RE, IR
(2)
16.4
CARTOSAT-2 1 5 P 9.6
CARTOSAT-2a <1 4 P 9.6
CARTOSAT-2B <1 4 P 9.6
SKYSAT-1 2 (0.9) <1 (hourly) B, G, R, IR, P 8
KOMPSAT-3 2.8 (0.7) 14 B, G, R, IR, P 16.8
KOMPSAT-2 4 (1) 14 B, G, R, IR, P 15
OrbView-3 4 (1) 3 B, G, R, IR, P 14
Satellite
Sensors
Resolution
Swath (km)
Spatial (m)* Temporal (days) Spectral (Bands)
CARTOSAT-1 (2.5) 5 P 30
FORMOSAT-2 8 (2) 1 B, G, R , IR, P 24
SPOT-5 5, 20 (2.5, 5) 2-3 G, R, IR, SW, P 60 to 80
SPOT-6 (1.5) 6 (1.5) 2-3 B, G, R , IR, P 60
RapidEye 5 1 B, G, R, RE, IR 77
RESOURCESAT-
1
5.8 5 G, R, IR 23, 70
GOKTURK-2 10, 20 (2.5) 2.5 B, G, R, IR, SW, P 20
TH-2 10 (2) B, G, R, IR,P 60
EROS-A (1.8) 2.1 P 14
Theos 15 (2) 3 B, G, R, IR 96
BEIJING-1 32 (4) 1 R G, IR 600
PROBA/HRC 18, 34 (5) 7 18 15

29. QB1- QuickBird (4 optical bands 2.62m)
WV2-WorldView2 (8 optical bands, 2.4m)
LS7-Landsat ETM+ (6 optical bands, 30m)
MO5-MODIS MOD09A1 (7 optical bands, 500m)
VIIRS-NPOESS VIIRS (11 optical bands, 375-750m)
Satellite Sensors Interoperability
Options for Up/Down Scaling

30. Large Scale Assessment: MODIS
@ 250/500-m spatial resolution
image credit: eomf.ou.edu

31. Large Scale Assessment: Landsat
@ 30-m spatial resolution
image credit: NASA

32. Large Scale Assessment: Sentinel 2
@ 10/20/60-m spatial resolution
Source: ESA/ESTEC
Wide Swath
Frequent Revisit
Days

33. Integrated Earth Observation System

34. Micro-
Hyperspec
(0.79 kg)
Portable Field Observation Systems
Era of UAVs
[Hyperspectral (ASD Field
Spec HH2, Spectral Evolution
SE-3500), NDVI Camera,
Green Seeker, GPS P&S
Cameras, HH GPSUs, Field
Data Kits, Galaxy Tabs, etc.]

35. Looking
East
Looking
South
Looking
West
Looking
North
Field
West
North
East
Down
South
1. Smartphone App “Field Photo”
2. Geo-referenced field photo library
3. Images (MODIS, Landsat, PALSAR)
Individual photos are linked with time series
MODIS data (2000-present)
Protocols
Citizen Science and Community RS
Georeferenced Field Photos

36. Build Collect Aggregate
Create Forms Collect Data Submit Data Analyze Data
House-hold
survey
Field Trials
Reporting
1. CRP DS Land use and land cover mapping (Jordan)
2. Pest and Disease Risk Mapping (Ethiopia, Morocco, Uzbekistan)
3. Small Ruminants Mapping (Jordan, Tunisia)
4. Food Security Project (Middle east and North Africa)
5. Forage and Feedstock Inventory (Afghanistan)
On the fly data
visualization
Grassland
degradation
Crop Types
Spatial distribution
Electronic Field Data
Collection Tools
Citizen Science and Community RS

37. (modified from Jensen, 2005)
Weather
prediction
Droughts
and floods
Crop areas
Crop types
Irrigated/rainfed
Land & Water
productivity
Trade-off in decision making
Biodiversity

38. Genetic
Approaches
Approaches @ different Hierarchy
Time Consuming; Due to High extinction rate? It is overtaking inventory process
Stratified approach; Extrapolation on large landscapes possible, Systematic Monitoring
and Spatial Environmental Database
HierarchyofBiologicalOrganisation

39. Environmental
Complexity
Disturbance
Regimes
Habitat
(Ecosystems)
Terrain
Climate
• Precipitation
• Temperature
• Radiation
• …
BIODIVERSITY
PRIORITY ZONE
Vegetation / Agro-Ecosystems
Landscape
Ecology
• Patch Characteristics
• Human Intervention
• …
Biodiversity Prioritization for Conservation

40. Just as the biome is primarily defined by its climate, the landscape
is primarily defined by the geomorphology, movement of water and
matter, the quality of the soils and the vegetation.
The vegetation, which is often used to characterize landscapes,
responds to the physical factors and the climate in an integrative
way.
Size Distribution Perimeter : Area Ratio
Boundary
Form
Patch
Orientation
Context
Contrast
Landscape matrix

41. Landscape matrix
FRAGMENTATION
Lowest HighestIntact
Natural Landscape Manmade Landscape
Intact Lowest Highest
POROSITY

42. FRAGMENTATION
The number of patches of one-habitat-type and
another type in per unit area.
F1..F4 : Forest types
Gr : Grassland
B : Blank
H : Habitation
W : Water Body
PATCHINESS
The measure of the density of patches of all
types or number of clusters in a given mask
W
F1
F2
F1
BGr
F2 F4
F3
H
500 m
500 m
NF
F
NF
500 m
500 m
W
F1
F2
F1
BGr
F2 F4
F3
H
500 m
500 m
F1..F4 : Forest types
Gr : Grassland
B : Blank
H : Habitation
W : Water Body
P =
 Di
n
I = 1
N
Fragmentation for above reclassified forest/ Non-forest map is 3
Patchiness of this figure is 10 within one grid
Nis number of boundaries between adjacent cells
Di is dissimilarity value for ith boundary between cells
Landscape matrix

43. POROSITY
The measure of number of patches or
density of patches within a particular type.
INTERSPERSION
The count of dissimilar neighbors with respect
to central pixel or measurement of the spatial
intermixing of the vegetation types.
Porosity of the patch F3 is 1.
243
151
121
3 X 3 window
W
F1
F2
F1
BGr
F2 F4
F3
H
500 m
500 m
F1..F4 : Forest types
Gr : Grassland
B : Blank
H : Habitation
W : Water Body
PO =  Cpi
n
I = 1
I =
 SFi
n
I = 1
N
Sfi is the shape factor.
Cpi is number of closed patches of ith class
Landscape matrix

44. 121
1F1
121
X (multiplied by) Relative weight of combination of two land cover type
Weightage matrix for juxtaposition
JUXTAPOSITION
The measure of proximity or adjacency of the vegetation types.
J =
 Di (Ji)
n
I = 1
J max
Di is the shape desirability weight for each cover type
Ji is the length of edge between combinations of cover types
Jmax is the average total weighted edge per habitat unit
Landscape matrix

45. Field Survey
Assignment of weights in SPAM model
Species area curve (herbaceous)
Vegetation profile
Nested quadrat
Assignment of weights
Field sampling stragegy

46. Diversity of the vegetation types

47. Habitat wise species richness

48. Mathematical
modeling
Spatial
Landscape
Analysis Model
(SPLAM)

49. Case Study: Genetic Resources Mapping

50. Land Use and Land Cover Mapping
Pakistan

51. Disturbance Index Map

52. Biological Richness Map

53. Existing NP
Existing WLS
Suitable site for
Estb. New NP & WLS
Biological Richness map showing GAP areas in Protected area network in Western Himalayan Region

54. Biological Richness in response to
Disturbance Index and Climate Change
(103km2)Area
Disturbance Index (DI)
Biological Richness Index (BRI)
Low Medium High Very High
15
10
5
0
0180000360000540000
Vegetationarea(Km2)
09001800
2700
Geographicarea(Km2)
6 10 14 18 22 26 30 34 68 72 76 80 84 88 92 96
Latitude (º) Longitude (º)
Species richness
Geographical area (km2 )
Vegetation area (km2)
Variables
without spatial
kernel
with spatial
kernel
Avg Temp. 0.35 0.65
Avg PPT 0.30 0.63
Max Temp. 0.33 0.64
Min Temp. 0.34 0.65
Min PPT 0.36 0.66
Avg Temp. + Avg PPT 0.37 0.69
Avg Temp.+ Avg PPT+Min Temp. 0.40 0.77
Avg Temp.+ Avg PPT+ Min
Temp.+ Max Temp. 0.44 0.83
Avg Temp.+ Avg PPT+ Min
Temp.+ Max Temp.+ Min PPT 0.50 0.88
Biradar et al., 2005
Roy et al., 2014*

55. Prioritization areas for Bioprospecting
Species Diversity
High Species & Genetic Diversity
Moderate Species & Genetic Diversity
Low Species Diversity Or Low Genetic Diversity

56. Conservation Prioritization
Biological Richness Map
Disturbance
Index Map
Distance from Road
(e.g. 1km)
Biodiversity
Conservation
Prioritization
Zones
Spatial Hemeroby
Index Map
Low Human Influence
(e.g. Low & Very low SHI)
Biological Richness
(e.g. High & Very-High BR regions
Low Disturbance
(e.g. High elevations)
Transportation
Networks
Drainage
System
Nearest water holes
(e.g. 500m, etc…)
Core Areas
Biodiversity Value
Species Richness
Ecosystem Uniqueness
High BV, SR & EU
(e.g. High & Very High value)

58. BCP Zones
Existing Protected Areas
Conservation Prioritization Zone Map
• Gaps in existing biodiversity
conservation network
• Half of the protected areas (PAs)
were not located in places that
contribute systematically to the
representation of the
biodiversity for the
conservation in the region
• High genetic diversity observed
in higher altitudes with less
disturbance

59. Conclusions
• Tools for Rapid Biodiversity assessment;
• Assess nature of habitat and disturbance regimes;
• Species – habitat relationship;
• Mapping biological richness and gap analysis; and
• Prioritizing biodiversity conservation and bio-prospecting
sites.
Geoinformatics provides base for designing long-term schemes
for biodiversity conservation;
• assessment of habitats, their quality and extent;
• determination of disturbance regimes;
• spatial distribution of biological richness;
• determination of the optimum size of conservation areas,
and
• identification of a set of species sensitive to particular
landscape function and structure.

60. Integrated Agricultural Production
Systems Approach
Crop Types, Pattern & Intensity
Terrain Complexity & Adaphic Profiles
Package of Practices
Biodiversity
and Crop
Improvement
Land and Water
Resources
Crop and
Livestock
Productions
Socio-
Economics,
Markets and
Policy
Improved Crop
Varieties
Viable Agronomic
Practices
Multi-purpose
Tree/Orchard Systems
Better Cropping
Systems
Increased Land &
Water Productivity
Livestock Production
Systems
Socio-Economic
Integrity )Index)
Profitable Market
Value Chains & Trade
Integrated Pest &
Disease Management
Land Tenure & Parcel Matrix
Climate Events & Trajectories
Location Based Service & Network Analysis
Land Suitability & Adoption Options
Expert Knowledge Base Systems
Socio-economy and demographic pattern
Out and Up-scaling
(Target Areas)
Priority Areas for Better
Interventions
M&E
Impact
Assessment
(Ex-ante)
Land Use and Land Cover Types
Multicriteria Geospatial Analysis
Layers of Package of Practices
Geoinformatics in Integrated Systems Approach
Innovation Platforms

61. Data Storage
Processing
Web Services
Linux (250TB)
Windows (100TB)
15070
25
50
20
30
Centralized Storage & Dissemination
Map ServerData Portals
Terminal Devices
Wheat
BarleyLentil
12 Cores CPU
48 GB RAM
Win 2012
8 Cores CPU
32 GB RAM
Win 2012
2xCPU (12 Cores)
512 GB RAM
Linux
Hosted in UK
Computing Servers
Printing Services
Large Format HR
printing and scanning
A3 HR printing
GU©2014
Field Equipment
[Hyperspectral, HH2, SE-
3500, NDVI Camera, Green
Seeker, GPS Cameras, HH
GPSUs, Field Data Kits, etc.]
Geo-Cyberinfrastructure
Geospatial Data Gateways
Backbone of the image processing
and mathematical modelling
[as of March 13, 2014]

62. http://geoagro.icarda.org
(beta)
Thank you
c.biradar@cgiar.org