ALEX HELD & MIKE GRUNDY

Transcription

1 ALEX HELD & MIKE GRUNDY

2 Context Why what is the niche? Where we are up to The scope, context and aims Progress to date GEOGLAM RAPP July 2014

3 Structural change From the 1970s to the 1990s, the consumption of meat in the developing world grew by 70 million tonnes, compared with only 26 million tonnes in the developed world Milk demand similar based on greater utilisation of traditional feed resources as well as an increased use of feed grains Demand has so far been met mainly from increased off-take rather than increases in productivity

4 GEOGLAM RAPP July 2014

5 The GEOSS rangelands / pasture lands task Complemented by: land condition activities where the focus is on desertification prevention, biodiversity protection and carbon storage; and soil degradation monitoring, eg. sensing ground cover dynamics, proportions of bare soil and green and senescent vegetation; forms of soil exposure Achieved by linkage to other activities

6 Global livestock production systems Robinson et al 2011

7 Can we improve these global maps? Passive microwave-based global aboveground biomass carbon dataset ( ) version 1.0 Liu, Y.Y., A.I.J.M. van Dijk, R.A.M. de Jeu, J.G. Canadell, M.F. McCabe, J.P. Evans and G. Wang Recent reversal in loss of global terrestrial biomass Nature Climate Change, 5, 2015 (doi: /NCLIMATE2581) Grazed biomass from livestock (Herrero et al PNAS 2013 )

8 Bringing it all together.. From GEOGLAM, GEOBON, GLOBIOM, ILRI / FAO Production stats, UNCCD, GAA, AGMIP, GEO WIKI, GRA,... Large gaps in knowledge extent, dynamics, biomass yield, utilisation, productivity, production... The GEOGLAM approach / solution is missing can a similar approach fill this gap and need? Provide a global, scientifically robust framework for monitoring food production from the world s grazing lands Coordinate and link activities that together can provide this framework Report regularly GEOGLAM RAPP July 2014

9 The GEOGLAM rangelands / pasture lands task Goal: Establish a dedicated global system for observing pastures and rangeland status, biomass dynamics and productivity Outcomes an improved capacity to manage risk and improve production of animal protein at a range of scales due to a better understanding of the trends in biomass and its use for protein production. the capacity to more effectively manage variability in production due to more timely and accurate national and regional agricultural statistical reporting and early warning of meat production shortfalls. more effective planning based on accurate forecasts of pasture and rangelands productivity variability. improved global understanding of risk across all landscapes as climate and land use change through the addition of these lands into global agricultural monitoring.

10 Key Implementation Elements 1. Establishment of Community of Practice (CoP): Via international workshops and partnership with governmental agencies and researcher community 2. Development of Proof-of-Concept Prototype Monitoring System Establish key global satellite data streams and derived products of value to the CoP via CEOS Monitoring system for global rangeland/grassland condition 3. Establish global Pilot Sites in main global grassland/livestock systems For satellite-data validation, in-situ field-data acquisition and productivity model testing 4. Integrating with Livestock Production Models into Visualization Dashboard and Reporting to e.g. FAO/AMIS

11 The GEOSS rangelands / pasture lands task Scale, frequency, predictions Finest scale, lowest frequency: 25m; yearly Broadest scale, highest frequency: 250m, seasonal Integrated simulation modelling probabilistic predictions components: Time series remote sensing of biomass and change in-situ measurement of above ground biomass Ancillary data: land use and tenure maps, pasture and woodland productivity models survey data of various kinds crucial to connect to data on animal distribution and movement climatological, soils and soil-moisture data local-to-global consumption and animal protein production

12 GEOGLAM RAPP July 2014

13 Satellite Data Acquisitions Global Baseline Core Satellite Data

14 Global Baseline Contributing Satellite Data

15 Example GEO GFOI Satellite Acquisition Plans Year Coverage added No. countries* Area* (Mkm 2 ) Total Area* (Mkm 2 ) 2013 GEO-FCT National Demonstrator Countries GFOI Participating Countries UN-REDD National Programme Countries WB-FCPF Participating Countries CD-REDD Project Countries (BMU) UN-REDD Partner Countries WB-FCPF Partner Countries Other Pan-Tropical Countries Global

16 Landsat 7 & 8 combined minimum cloud cover Baseline 2014: Landsat Coverage

17 Several GFOI priority areas have been acquired during the Sentinel-1 ramp-up Sentinel-1 phase for (overall GFOI/ dual-pol scenes): Colombia: 698 / 22 Ecuador: 340 / 0 Peru: 826 / 12 Tanzania: 684 / 61 Kenya: 369 / 34 Uganda: 316 / 30 Cameroon: 21 / 11 DRC: 290 / 36 Cambodia: 76 / 50 Viet-Nam: 371 / 142 Sumatra: 495 / 189 Status 19 March 2015

18 Organisation and governance Initial stages: Australian interim RAPP task team (CSIRO, Qld Govt, Univ Qld, Aust Govt) Working group representing a developing community of practice Eventual model Steering and Advisory Group (key global institutional partners, donors and users) RAPP Technical Implementation Group (task and pilot project leaders)

19 Progress to-date Establishment of Community of Practice: Held two workshops (Sydney & Paris) to establish community of practice (RAPP CoP) and common vision for RAPP Workshop in Campinas, Brazil Development of Proof-of-Concept Prototype Monitoring System Leveraging from GEOGLAM Visualisation Dashboard Development of satellite data acquisition plan for RAPP Global derived products: Fractional Cover & standing biomass Agreement on Rangelands and Pasture Mask Initial pilot sites identified Coordination needed on satellite-data validation, in-situ field-data acquisition and productivity model testing (Australia, Canada, China, Brazil, Argentina, South Africa, etc.) Evaluating and Integrating Global Rangeland and Livestock Model(s) into Dashboard Urgent need to form a structure for better coordination and co-investment

20 Mockup Interface 4 classes of Rangelands (WWF)

21 GEO X RAPP outline