Spatial Data Infrastructures in Support to Land Cover Monitoring 2016-01-26¢  Spatial Data Infrastructures

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  • Spatial Data Infrastructures

    in Support to Land Cover Monitoring Activities

    Brice Mora, PhD

    GOFC-GOLD Land Cover Project Office

    Spatial Data infrastructures Course (MGI), January 2015

  • Outline

    • What is GOFC-GOLD?

    • A Web-portal for Global Land Cover Reference Datasets

    - data quality standards

    - good accuracy assessment practices

    - data distribution licensing

    - introduction to relational database concepts

    • Data Stream Management in the Context of Forest Monitoring

    - forest change detection with remote sensing

    - smartphone applications

    - community-based monitoring

  • What is GOFC-GOLD?

    • Developed in 1997, originally under the Committee on Earth Observation Satellites (CEOS):

    - To test the concept of an Integrated Global Observing System (IGOS)

    - To improve use of Earth Observation data to address major problems of global concern

    - To improve coordination of national programs

    - To improve co-operation between providers and users of Earth Observation data for regional and global applications

    • Has become one of the Panels of the Global Terrestrial Observing System GTOS (FAO GTOS Secretariat)

    • Sponsors: FAO, WMO, UNEP, UNESCO, ICSU, EC-JRC, ESA, NASA, USGS

  • Background to GOFC-GOLD

    1. RedLatiF – Latin America

    2. SAFNET – Southern. Africa

    3. Miombo – Southern Africa

    4. OSFAC – Central Africa

    5. WARN – West Africa

    6. SEARRIN – S.E. Asia

    7. NERIN – Northern Eurasia

    8. CARIN – Central Asia

    9. SCERIN – S.Central Europe.

    10. SARIN – South Asia

    11. BARIN – Baltic-Arctic

    GTOS

    User

    Outreach

    Science and

    Technical Board

    GOFC-GOLD

    Executive Committee Project Office

    Implementation

    Teams

    - Land Cover

    - Fire

    Partnerships e.g. UNISDR WFAG;

    CEOS WGCV

    Global Strategies &

    Frameworks e.g. GEOSS, GCOS

    IP

    Regional

    Networks

    Working Groups

    - REDD

    - Biomass

  • A Web-portal for Global Land Cover

    Reference Datasets

  • Data portal: Background

    • Observation of land cover at global scale useful for many scientific and managerial applications

    • Several global land cover (GLC) maps produced using remote sensing data

    • Several independently validated GLC datasets which generation required significant efforts to analyse a large number of satellite images and interpret land cover type

    • Reference datasets not always easily accessible to the land cover community and not used to full potential despite scarcity of such datasets

  • Data portal: objectives

    The GOFC-GOLD Land Cover Office web portal aims to: • Provide most appropriate databases based on internal quality

    criteria and a consolidation • Allow open and easy access to available datasets to the land

    cover community (while keeping some data for independent assessments)

    • Foster use of recommended practices for land cover validation in the GLC mapping community

    • Each dataset provided along with detailed information and recommendations for an appropriate use

    • Direct users to most appropriate dataset(s) according to specific needs

    • Basis for more operational land cover validation activities of GOFC-GOLD

  • GOFC-GOLD reference data web portal

    http://www.gofcgold.wur.nl/

    http://www.gofcgold.wur.nl/ http://www.gofcgold.wur.nl/

  • GOFC-GOLD reference data web portal

    https://cartodb.com/

    https://cartodb.com/ https://cartodb.com/

  • Promoting Standards:

    Land Cover Classification system

    • Developed by FAO and UNEP as comprehensive and standardized classification system for mapping purposes.

    • Independent from mapping scale

    • Allows dynamic creation of classes using combination of LC diagnostic attributes called classifiers.

    • Last version of the LCCS: LC Metadata Language (LCML – LCCS v.3) proposed as standard by the International Organization for Standardization (ISO): ISO 19144-1.

    • Complementary specifications under development (reference WI 19144-2).

    • Herold, M., Hubald, R., & Di Gregorio, A. (2008). Translating and evaluating the land cover legends using the UN Land Cover Classification System (LCCS). Network (p. 189). Jena, Germany. http://nofc.cfs.nrcan.gc.ca/gofc-gold/Report%20Series/GOLD_43.pdf

    http://nofc.cfs.nrcan.gc.ca/gofc-gold/Report Series/GOLD_43.pdf http://nofc.cfs.nrcan.gc.ca/gofc-gold/Report Series/GOLD_43.pdf http://nofc.cfs.nrcan.gc.ca/gofc-gold/Report Series/GOLD_43.pdf http://nofc.cfs.nrcan.gc.ca/gofc-gold/Report Series/GOLD_43.pdf http://nofc.cfs.nrcan.gc.ca/gofc-gold/Report Series/GOLD_43.pdf http://nofc.cfs.nrcan.gc.ca/gofc-gold/Report Series/GOLD_43.pdf http://nofc.cfs.nrcan.gc.ca/gofc-gold/Report Series/GOLD_43.pdf http://nofc.cfs.nrcan.gc.ca/gofc-gold/Report Series/GOLD_43.pdf http://nofc.cfs.nrcan.gc.ca/gofc-gold/Report Series/GOLD_43.pdf http://nofc.cfs.nrcan.gc.ca/gofc-gold/Report Series/GOLD_43.pdf http://nofc.cfs.nrcan.gc.ca/gofc-gold/Report Series/GOLD_43.pdf http://nofc.cfs.nrcan.gc.ca/gofc-gold/Report Series/GOLD_43.pdf http://nofc.cfs.nrcan.gc.ca/gofc-gold/Report Series/GOLD_43.pdf http://nofc.cfs.nrcan.gc.ca/gofc-gold/Report Series/GOLD_43.pdf http://nofc.cfs.nrcan.gc.ca/gofc-gold/Report Series/GOLD_43.pdf http://nofc.cfs.nrcan.gc.ca/gofc-gold/Report Series/GOLD_43.pdf http://nofc.cfs.nrcan.gc.ca/gofc-gold/Report Series/GOLD_43.pdf http://nofc.cfs.nrcan.gc.ca/gofc-gold/Report Series/GOLD_43.pdf http://nofc.cfs.nrcan.gc.ca/gofc-gold/Report Series/GOLD_43.pdf http://nofc.cfs.nrcan.gc.ca/gofc-gold/Report Series/GOLD_43.pdf

  • Available datasets

    Name Sampling design Sample size Sample

    unit/size

    Source or reference

    data

    Legend

    protocol Legend Rreference

    GLC 2000 2 stage stratified

    cluster sampling

    1265

    253 PSU

    5SSU in each

    PSU

    3by3 pixels

    Landsat 2000, aerial

    photographs, thematic

    maps, NDVI profile

    If many LC types

    are there, 2

    main covers

    were recorded

    >80%

    LCCS 22

    class

    Mayaux et al

    2006

    GlobCover Stratified random

    sampling

    4258

    3167 certain

    5by5 pixel SPOT VGT-NDVI profile,

    Google Earth,

    >75%

    dominance,

    Record more

    classes if there is

    LCCS 22

    class

    Defourny et al

    2009

    STEP stratified 1780

    Landsat, high and low

    resolution images

    (Google Earth)

    Google Earth

    IGBP 17

    classes +

    other classes

    Friedl et al.,

    2000

    Sulla-Menashe

    et al., 2011

    VIIRS

    stratified random

    sampling

    500 5by5 km

    blocks VHSR (

  • Upcoming datasets and future updates

    Dataset name

    Provider Sample size Suitable for Coverage Reference

    VHSR Boston U. 500 Validation Worldwide Olofsson, et al., 2012

    GlobCover 2009

    ESA/UCL Validation Worldwide Bontemps, et al., 2011

    Landsat GLC map

    China 38664 Validation Worldwide Gong, et al., 2013

    LC CCI ESA 13000 Validation Worldwide Achard et al., 2011

    NELDA dataset

    NELDA 11 Validation Northern Eurasia

    Clark & Aide,

    2011b

    • Regular updates coming from: VIIRS, STEP

  • Distribution License

    Datasets distributed under Creative Commons License: Attribution-Non Commercial-No Derivatives

    http://creativecommons.org/

    http://creativecommons.org/ http://creativecommons.org/

  • Providing guidance: Metadata

    Difficult or impossible to use data without proper metadata

    See ISO 19115 and ISO 19139 standards

  • Providing Guidance: Suitability of GLCR datasets for

    different users Metadata of GLCR

    datasets User requirements for

    reference datasets

    User requirement criteria

    Evaluation of GLCR datasets for each user requirement criteria

    Combining criteria performance using multi-criteria approach

    Criteria Probability

    sampling

    Class

    representatio

    n

    Easily combined

    and augmented

    sampling

    scheme

    Classification

    scheme and

    thematic details

    of the legend

    Hierarchical

    classifiers

    provided

    Temporal

    coverage

    Stable

    multi- date

    sample

    Spatial

    resolution Verified

    Interpreta

    tion

    confidenc

    e

    recorded

    IGBP-DIS ++ - + +/- ++ - -- ++ + ++

    GlobCov5 ++ +/- + ++ ++ + - ++ ++ ++

    GlobCov9 ++ + + ++ ++ + - ++ - ++

    GLC2000 ++ +/- + ++ ++ + -- - ++ -

    GLCNMO-

    val ++ +/- + ++ - + -- + - -

    GLCNMO-tr -- +/- -- +/- - + -- +/- +/- -

    MODIS-tr -- +