spatial data infrastructures in support to land cover monitoring … · 2016-01-26 · spatial data...
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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
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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
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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
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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
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A Web-portal for Global Land Cover
Reference Datasets
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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
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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
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GOFC-GOLD reference data web portal
http://www.gofcgold.wur.nl/
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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
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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 (<2-m)
manual
interpretation
aided by Google
Earth and MODIS
time-series
IGBP legend,
and LCCS in
the future
Olofsson et al.,
2012
Stehman et al.
2012
GLCNMO
International Steering
Committee for Global Mapping
600 Training/Vali
dation Worldwide
Tateishi, et al., 2011
GLCNMO
International Steering
Committee for Global
Mapping
Global urban
ground truth data
University of Tokyo
3734 Validation Worldwide
Global urban ground truth
data
University of Tokyo
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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
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Distribution License
Datasets distributed under Creative Commons License: Attribution-Non Commercial-No Derivatives
http://creativecommons.org/
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Providing guidance: Metadata
Difficult or impossible to use data without proper metadata
See ISO 19115 and ISO 19139 standards
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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 -- +/- -- +/- ++ + - +/- + -
FAO-FRA ++ ++ +/- +/- - + ++ ++ + -
LC-CCI ++ ++ +/- ++ ++ ++ ++ ++ + ++
GOFC-GOLD ++ + ++ +/- ++ + - ++ + ++
GEO-WIKI -- + -- + +/- + - + - ++
VIEW-IT +/- + - - - + - -- + ++
(++ Highly suitable, + Very suitable, +/- Moderately suitable, - Marginally suitable, -- Not suitable )
Criteria performance of GLCR datasets for each user requirement criteria of Climate modelling users
Tsendbazar et al., 2013
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Suitability of GLCR
datasets for
different users
The LC-CCI, GOFC-GOLD, FAO-
FRA and Geo-Wiki datasets were
generally more suitable for re-
use than the other datasets.
The analysed datasets are
generally more suitable for
agricultural monitoring and
improving GLC maps.
Climate modelling community
and forest change analysis
require stable multi-date sample
which couldn’t be met many of
the GLCR datasets.
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Data Storage, Retrieval, and Visualisation
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Web-GIS application
• For data storage, retrieval,
and visualization
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Relational Model in PostgreSQL
Following BOYCE-CODD Normal Form underlined: primary/secondary key(s), #: foreign key, table names in capital letters INSTITUTION (Iinstitution, Iname)
TITLE (Ttitle, Tname)
PERSON (Pperson, Pinstitution#, Ptitle#, Pfirstname, Plastname, Pemail, Pprofile#, Plogin, Ppwd)
DATASET (DAdataset, DAname)
ACCESS (Aaccess, Aname)
DATA (Ddata, Daccesstype#, Duploaddate, Dthemayear#, Dcountry#, Ddataset#, Ddatalink, Dmetadatalink)
COUNTRY (Ccountry, Ccharid, Cname)
OWN (Operson#, Odata#)
BVHRIM (BVImage, BVIdata#, BVIsite#, BVItype#)
SITE (Ssite, Slatcentroid, Slongcentroid, Secoclimate#)
ECOCLIM (Ecoclim, Ename)
BVHRPROCES (BVPimage#, BVPimageori#, BVPperson#, BVPdatebegin, BVPdatend, BVPsoft#, BVPtrain#, BVPaccuracy, BVPcomments)
SOFTWARE (SOsoft, SOname) Theory and practice example
https://www.youtube.com/watch?v=hTFyG5o8-EA
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Database: Forms to upload and retrieve
information
Form to enter new information on
persons, affiliations, sites, software
not referenced in database yet
Based on SQL (Structured Query Language)
=> Enables creation and manipulation of tables in a database
AND
=> Query the tables to retrieve information
SQL query example:
https://www.youtube.com/watch?v=aZekk0udLYg
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Needs from the user communities
Better guidance on how to use reference datasets - thematic applications (biodiversity, crop monitoring, ...)
- best land cover map accuracy assessment practices
Online data portal enabling upload of map products for accuracy assessment.
Independent reference dataset as benchmark for GLC maps
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Data Stream Management in the Context of Forest Monitoring
Credits: Arun Pratihast, Post-doc researcher at WUR
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Distribution of Aboveground Forest Biomass
Source: Avitabile et al. 2015
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Forest Change Patterns 2000 – 2005
Source: FAO/FRA RSS, 2012
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Climate Mitigation Mechanism: REDD+
United Nations Framework Convention on Climate Change – Cancun agreements on REDD+ (UNFCCC, 2010)
Following activities are included:
● Reducing emissions from deforestation
● Reducing emissions from forest degradation
● Conservation of forest carbon stocks
● Sustainable management of forest
● Enhancement of forest carbon stocks
REDD
+
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Need for a Monitoring System:
Complementarity of Data Streams
The relative strength of contribution of each data stream to the REDD+ MRV objectives is indicated by shade (dark = strong; light = limited)
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BFAST Spatial: in-Migration (Kafa, Ethiopia)
Image background: SPOT5 (Feb 2011,
2.5m)
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Technical Setup for Forest Monitoring
Data collector : local expert
Systematic form design : decision based form design for Mobile
device
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Interactive Forest Monitoring System
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Web Resources
• Change detection and monitoring (BFAST):
http://bfast.r-forge.r-project.org/
https://github.com/dutri001/bfastSpatial
http://www.wageningenur.nl/en/Expertise-Services/Chair-
groups/Environmental-Sciences/Laboratory-of-Geoinformation-Science-and-
Remote-Sensing/Research/Integrated-land-
monitoring/Change_detection_and_monitoring.htm
• GOFC-GOLD REDD sourcebook:
http://www.gofcgold.wur.nl/redd
• Organizing community level forest surveys for REDD+: Manual for Community
Technicians
• http://redd.ciga.unam.mx/files/CommunityManual.pdf
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