Global Research Project 4:

Reducing land health risks and targeting Agroforestry interventions to enhance land productivity

• Develop methods for evidenced-based management of land health – Land Health Surveillance

• Apply methods to multi-scale targeting of sustainable land management & assessing intervention outcomes

Vagen

Thomas Gumbricht; Tor-Gunnar Vagen; Jianchu Xu; Emias Betemariam; Andrew Sila; Spectral Lab Staff; Keith Shepherd

Jeremias Mowo; Joy Turkihawa; Athanase Mukuralinda; Zac Tchoundjeu; Bertin Takoutsing; Christophe Kouame; Gudeta Sileshi; Tracy Beedy; Pal Singh; Sonya Dewi; Ric Coe; Anja Gassner

Land Health - the capacity of land to sustain delivery of essential ecosystem services (the benefits people obtain from ecosystems)

Surveillance Science Principles

• Measure frequency of problems and associated risk factors in populations using statistical sampling designs & standardized measurement protocols

• Assess association between problems and risk factors using statistical models

• Rigorously evaluate interventions using experimental designs with controls

• Meta-analysis is the primary information source for designing policy/programmes

• Operational surveillance systems are built into everyday policy and practice

?

Cost surfaces, etc.

Regional Spatial Information Systems

Elevation

Vegetation

Hydrology

Topographical properties

Climate

Landsat

Legacy data

ASTER

Quickbird

MODIS

500 m

250 m

28.5 m

15 m

2.4 m

0.6 m

Sentinel Site Surveillance Framework

a spatially stratified,

hierarchical, randomized

sampling framework

Sentinel site (100 km2)

16 Clusters (1 km2)

10 Plots (1000 m2)

4 Sub-Plots (100 m2)

Soil-Plant Spectral Diagnostics

• Spectral methods and decision support tools

• Reference lab for AfSIS

• Capacity building

Scientific workflows

AfSIS IR spectralprediction engine

Digital mapping of land health

Topsoil soil organic carbon (g kg-1) for Kipsing derived by statistical modelling of georeferenced soil

carbon estimates to reflectance values from a QuickBird satellite image

Automated reporting

AfSIS External ReviewPioneering unique effort• Pioneering effort intended to fill one of the major gaps in spatial information worldwide

• Unique scientific effort never attempted before. Admirable first done in Africa.• Highly motivated team of creative scientists.• Well on track to deliver on its major goals

Outstanding design & implementation• Well-documented, unique soil health surveillance methodology.• Outstanding design and implementation of field surveillance under very difficult conditions.

• Sentinel sites will likely become long-term monitoring and research sites for many different purposes.

Soil spectroscopy lab a pioneering facility / Excellent training of NARS• Systematic use of IR spectroscopy is groundbreaking in the world of soil testing. • The ICRAF soil spectroscopy lab is a pioneering facility and many experts are taking notice.

• Great progress in building soil spectral libraries for functional interpretation.• Good, well-documented workflows and quality control protocols. • Excellent training of NARS collaborators provided by the ICRAF lab.

• Excellent potential for digital soil mapping in Africa / Large spill over effects• Excellent potential to link the soil spectral analysis information with higher-resolution remote sensing data for digital soil mapping in Africa through automated mapping techniques.

• Large spillover effects due to other projects and initiatives adopting the methodologies

Land Health Out-scaling

Tibetan Plateau/ Mekong

Africa Soils Information Service

Cocoa - CDIParklands Malawi

National surveillance systems

Regional Information Systems

Project baselines

Rwanda, Ethiopia

Rangelands E/W AfricaSLM Cameroon MICCA EAfrica

Global Agricultural Monitoring System – Gates - CI

Great Green Wall

New Digital Soil Map of the World

Tree Density Mapping at Fine Resolution

Map of tree density in an areas with steep climatic gradients in northern Kenya, derived from modelling ground data collected from sentinel sites to

Landsat imagery (28.5 m resolution).+ Mapping tree and land cover affected by plantation economy in Amazon, Congo, Mekong (Jianchu, Zac, Roberto)+ Great Green Wall Baseline proposal (Gumbricht, Vagen, et al)

Protocol for Measuring Soil Carbon in Landscapes

1988

2006

Latin America – Amazon Information System

GIS datasets• Vector datasets (infrastructural, political,

biophysical)

Species occurence database• 150,000 geo-referenced species occurrence

records • 179 Agroforestry tree species

Downscaled climate data• Scenario SRES A1B • 5 GCMs (CNRM, CSIRO, ECHAM5, MIROC3)• 2030, 2050, 2080

Satellite derived data• MODIS 1km² data(EVI, NDVI, LAI, FPAR, NPP

etc.)• Cloud-free LANDSAT –Mosaics

Konstantin König – k.koenig@cgiar.org

High resolution species

distribution maps

Predictions of species

distribution and biome

shifts under CC

Empirical modelling

Modelling spread of plantation rubber and associated forest loss in Xishuangbanna,

China

1988

2006

Environmental space occupied by rubber through time

CRP5: Water, Land & Ecosystems

Information Systems forLand, Water & Ecosystems

Outcomes

• A wide range of stakeholders have access to high quality spatial information and decision support systems on land and water resources condition/trends and intervention performance

• Scientifically sound planning, implementation, and evaluation of land and water management policy and practice

VisionNatural resource and environmental policy and management decision making in agriculture and associated areas is increasingly based upon sound scientific evidence

CRP5 Priority Basins

Africa SoilInformatio

nService

Agro-ecological Information SystemCRP5 Water, Land, Ecosystems

Strengthening water surveillance: (i) remote sensing of components of water balance; (ii) standardized datasets of simulated water data at fine spatial resolution. Landscape genomics.

The foundation of ICRAF’s research are trees as an object (what?) in space (where?) and time (when?) linked to function (so what?) and drivers (why/why not?), which makes quantifying local, national and global benefits of trees a multivariate spatio-temporal question.

From the extensive work with spatial data within GRP4, ICRAF launched a new Geoinformatics unit 1st June 2011.

The rationale for ICRAF to create a Geoinformatics unit is resting on the fact that the bottleneck for using spatial data is no longer data cost or availability, but rather lack of consistent and comprehensive processing, analysis, visualization, mining and dissemination methods. Hence the emphasis of the proposed strategy is on adopting and implementing scientific methods that are normally not used in combination, and to produce quality tagged spatial datasets that are then analyzed and visualized using state-of-the-art scientific methods.

ICRAF Geoinformatics Unit

Initially the Geoinformatics unit will concentrate on developing service functions for ICRAF researchers, including: Develop an open source based geo-catalogue of existing spatial data holdings Develop a web-based interface for searching geospatial data Develop a logical structure for a spatial data repository Develop a web-interface allowing in-house access to a set of standard maps

Further tasks includeSetting up a web map server allowing all users to generate customized mapsCreating a web-interfaced spatial relations modeling and hypothesis testing toolGiving access to advanced in-house users to use a desk-top Geographical

Information System (GIS) for analyzing the spatial dataConnecting the spatial data holdings and models to scientific workflows for

automatic analysis of continental to global datasets.

ICRAF Geoinformatics Unit

For those interested in the development of spatial data processing and access, the Geoinformatics unit will host three of the method breakout events on Tuesday and Wednesday.

A6. Geoinformatics 1. Monitoring vegetation annual phenology from time series of satellite imagery

B5. Geoinformatics 3. ICRAF online spatial data infrastructure - development of new web-tool for supporting research

C4. Geoinformatics 2. Automatic derivation of landscape biophysical characteristics from satellite images

ICRAF Geoinformatics Unit