THEME – 1 Geoinformatics and Genetic Resources under Changing Climate
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THEME – 1 Geoinformatics and Genetic Resources under Changing Climate THEME – 1 Geoinformatics and Genetic Resources under Changing Climate Presentation Transcript

  • 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
  • River Flow Ecosystem Energy Habitat Delta Communication Agriculture Groundwater Water use Flood Drought Earth Observation Horticulture Weather Biodiversity
  • 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 View slide
  • 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 View slide
  • 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
  • Biological Richness Human Induced Disturbance Mittermeter, 1998 Hannah & Lohse, 1993 Sunlight Water Temperature Potential Climate Limits Ever Changing Climate Genetic Resources in Peril
  • Change in Precipitation 1980/1999 to 2080/2099 Global Scale
  • Change in Temperature 1980/1999 to 2080/2099 Global Scale
  • 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
  • MODIS-USGS 1993-2003 MODIS 2001-2002 MODIS 2002-2003 Bareland Forest-BarelandForest Climate extremes (changes in Rainfall) and Land use change
  • National to Local Scale Climate Extremes Climate Deviation from long- term mean
  • Vulnerability Index Low High Vulnerability of crops to Pests & Diseases Stripe Rust in Wheat
  • Reflectance-Based Characterization of Wheat Cultivars for Identifying Drought Tolerance
  • 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)
  • 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
  • Vegetation vs. Species Richness
  • 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?
  • ARIMA (Autoregressive Integrated Moving Average) model analysis for the period of 2000 to 2013 to forecast Climate Change Impact?
  • Mapping, Monitoring, Management,… Never Ending? Biodiversity Evaluation Biodiversity Management
  • 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”
  • 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
  • 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
  • Role of Remote Sensing in Landscape Ecology
  • 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
  • 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…
  • 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
  • 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
  • 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
  • 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
  • Large Scale Assessment: MODIS @ 250/500-m spatial resolution image credit: eomf.ou.edu
  • Large Scale Assessment: Landsat @ 30-m spatial resolution image credit: NASA
  • Large Scale Assessment: Sentinel 2 @ 10/20/60-m spatial resolution Source: ESA/ESTEC Wide Swath Frequent Revisit Days
  • Integrated Earth Observation System
  • 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.]
  • 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
  • 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
  • (modified from Jensen, 2005) Weather prediction Droughts and floods Crop areas Crop types Irrigated/rainfed Land & Water productivity Trade-off in decision making Biodiversity
  • 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
  • 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
  • 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
  • Landscape matrix FRAGMENTATION Lowest HighestIntact Natural Landscape Manmade Landscape Intact Lowest Highest POROSITY
  • 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
  • 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
  • 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
  • Field Survey Assignment of weights in SPAM model Species area curve (herbaceous) Vegetation profile Nested quadrat Assignment of weights Field sampling stragegy
  • Diversity of the vegetation types
  • Habitat wise species richness
  • Mathematical modeling Spatial Landscape Analysis Model (SPLAM)
  • Case Study: Genetic Resources Mapping
  • Land Use and Land Cover Mapping Pakistan
  • Disturbance Index Map
  • Biological Richness Map
  • 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
  • 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*
  • Prioritization areas for Bioprospecting Species Diversity High Species & Genetic Diversity Moderate Species & Genetic Diversity Low Species Diversity Or Low Genetic Diversity
  • 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)
  • 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
  • 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.
  • 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
  • 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]
  • http://geoagro.icarda.org (beta) Thank you c.biradar@cgiar.org