Blending Methodologies for Multiple Earth Observation Products

Eric A. Lehmann – CCI, Floreat WA

EARTH OBSERVATION INFORMATICS TCP

EOI TCP Workshop | 24 - 25 September 2013 | Canberra

Project Overview

• Team members: Tim McVicar (CLW), Grace Chiu (CCI), Irina Emelyanova (CLW), Tom Van Niel (CLW), Shuvo Bakar (CCI)

• Main collaborator: Brent Henderson (CCI)

• Project objectives: • assessment and further development of existing/new data blending

methodologies for EO products

• provide unified framework for blending/fusion of EO datasets into relevant information products

• strengthening of EOI capabilities across CSIRO; bring together existing remote sensing, informatics/analytics and IT skills

• identify the needs and priorities of end-users of data fusion products

EOI TCP Data Blending Workshop | Eric Lehmann 2 |

Data Blending Workshop • Held on April 22–24, 2013, Black Mountain Labs, Canberra

• 23 participants, key CSIRO Groups/Divisions and external agencies

• 13 presentations and associated discussions: – blending methodologies and applications for EO data

– operational and computational aspects

• Workshop program and links to presentations available online on the EOI wiki (https://wiki.csiro.au/display/eonetwork)

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BUSINESS UNIT / ORGANISATION (NR. OF REPRESENTATIVES)

REPRESENTATIVES

1. CMIS (9) G. Chiu, M. Dobbie, B. Henderson, W. Jin, P. Kokic, E. Lehmann, L. Murray, R. Shah, D. Watkins

2. CLW (7) A. Dekker, I. Emelyanova, L. Li, T. McVicar, L. Renzullo, N. Sims, T. Van Niel

3. IM&T eResearch/ASC (3) J. Bowden, L. Domanski, J. Morrissey

4. CSS TCP (1) L. Murray (also CMIS)

5. EOI TCP (1) A. Dekker (also CLW)

6. CESRE (1) J. Vote

7. Geoscience Australia (3) L. Lymburner, J. Sixsmith, L. Wyborn

Activity Report Technical report: main output from workshop and EOI TCP Activity

Lehmann, E.A., McVicar, T.R., Chiu, G.S., Emelyanova, I.V., Van Niel, T.G., Bakar, K.S. and Henderson, B.L. (2013). Blending Methodologies for Multiple Earth Observation Products: Activity Report to CSIRO’s Earth Observation Informatics Transformational Capability Platform. EOI TCP Client Report EP138192, CSIRO, Australia, 34 pages. Publically available in e-Publish: https://publications.csiro.au/rpr/pub?pid=csiro:EP138192

Main contents: • Research and operational gaps • Benefits to CSIRO, Australia and the

wider scientific community • Set of (engineering and scientific)

recommendations for future research on EO data blending

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Relevance to Scientific Community The Data Blending workshop identified a wide array of past/existing CSIRO projects making use of blended EO products: • Crop yield modelling and inundation mapping: blended Landsat–

MODIS data for landscape studies and community-level management

• Water resources accounting and assessment: blended soil moisture product from various EO and in-situ datasets

• Reef rescue research and development: assessment of water quality using combination of EO and in-situ measurements recorded at different spatial/temporal scales

• Environmental monitoring including ozone concentration, lower atmosphere CO2, and aerosol optical depth, using various ground-based and EO datasets

• Assessment of climate impacts by blending/reconciling multiple outputs from climate models

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Benefits to CSIRO & Scientific Community

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External Science and Business Development Opportunities

SAF –crop yield monitoring

and forecasting –assessing landscape

degradation dynamics –productivity

evaluation in native vegetation systems

–fire fuel load estimation

–down-scaling biomass estimates from 1 km to 25m resolution

–soil carbon assessment

WfHC –monitoring flood and

floodplain dynamics –assessing riparian

vegetation health –estimation of actual

evapotranspiration –on-farm water

storage dynamics –mapping groundwater

dependent ecosystems

–water resources accounting and assessment

CAF –assessing habitat

dynamics –evaluating urban and

built environment changes

–etc.

MDU –mine-site

rehabilitation –Exploration –etc.

WfO –coastal water quality

dynamics – inland water quality

dynamics –bathymetric mapping

of near-coastal waters –monitoring algal

blooms –etc.

EOI TCP Blending Methods and Infrastructure

Identified Data Fusion Needs • Importance of providing measures of uncertainty (error

characterisation) for the blended products

• Operational viability of data fusion algorithms (mainly in terms of computational complexity)

• Need for the fusion methods to be as generic as possible

• Relevance with respect to future datasets (current and future EO systems/sensors with varying spatial, temporal and spectral resolutions)

• Data fusion methods to account for all attributes of the blended datasets (i.e. spatial, temporal, spectral and radiometric), ideally simultaneously

EOI TCP Data Blending Workshop | Eric Lehmann 7 |

Existing Blending Methods Two main strategies for EO data fusion were identified: • ALMBI methods (Australian Landsat–MODIS Blending Infrastructure)

– linear interpolation model

– global empirical image fusion model

– spatial/temporal adaptive reflectance fusion model (STARFM)

– enhanced STARFM

• algorithms used specifically for Landsat and MODIS data within ALMBI

• some weaknesses, e.g. no uncertainty measures

• ready for large-scale operationalisation!

• Statistical inference methods • unified framework for EO data fusion

• rigorous quantification of uncertainty

• high computational costs

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Existing Operational Resources Computational and data-related issues represent important aspects of EO data fusion. Existing resources include:

• Virtual Geophysics Laboratory (VGL): scientific workflow portal

• ‘Workspace’: tool for analysis, collaboration and commercialisation of code and user products

• High-performance computing (HPC) capabilities at CSIRO: IM&T’s eResearch and Advanced Scientific Computing (ASC) teams

• Availability of large datasets of processed and quality-checked EO data: e.g. Landsat Datacube from Geoscience Australia, with infrastructures for data storage and access

• Software packages for Bayesian modelling and computation: e.g. ‘LibBi’ software by L. Murray (CSS TCP), R package ‘spTimer’ by S. Bakar (CCI).

→ Key linkages have already been established in past/existing projects!

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Recommendations for Data Blending Research

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YEAR 1 YEAR 2 YEAR 3 YEAR 4 YEAR 5

Engin

eering

Implementation of

ALMBI

1 FTE p.a. (12 months)

Implementation of integrated spatio-temporal BHM

0.5 FTE p.a. (36 months)

Engineering activities to support other scientific activities

0.5 FTE p.a. (36 months)

Address computational and software-related issues | Enable operational implementation of

data fusion methods | Build systematic summary of data fusion vocabulary

Scie

ntific r

esearc

h

Informal quantification of

uncertainty in ALMBI methods 1 FTE p.a. (18 months)

Preliminary BHM for

Landsat–MODIS fusion

1.5 FTE p.a. (18 months)

Develop integrated spatio-temporal BHM framework & application

to (generic) case-studies / datasets (EO/in-situ)

1.5 FTE p.a. (36 months)

Formal analysis of uncertainty in ALMBI

1 FTE p.a. (24 months)

Integrate spectral + radiometric domains into ALMBI methods 1.5 FTE p.a. (36 months)

Study of existing and emerging methods | Literature review | Monitor research and

industry needs | Develop methods for model and product validation

SHORT TERM LONG TERM

Data fusion needs: • measures of uncertainty • generic methods (future datasets) • operational viability • integrate all attributes of EO data

CSIRO Computational Informatics Dr. Eric A. Lehmann

t +61 8 9333 6123 e Eric.Lehmann@csiro.au w www.csiro.au

EARTH OBSERVATION INFORMATICS TCP

Thank you