“Global and regional land cover and land change monitoring:

progress and needs”

Martin Herold

Wageningen University

(martin.herold@wur.nl)

www.fao.org/gtos/gofc-goldGlobal Observations of Forest Cover and Land Dynamics

What is GOFC-GOLD?• A technical panel of the UN Global Terrestrial Observing

System (GTOS/FAO)

• A coordinated international effort: – to ensure a continuous program of space-based and field forest and

land observations for global monitoring of terrestrial resources

• A network of participants implementing coordinated research, demonstration and operational projects

• A vision to share data, information and knowledge

• GOFC-GOLD operates through:– Working with GEO (tasks) and GCOS

– Executive committee, science and technical board

– Implementation teams and 3 project offices (CA, US, Europe)

– Dedicated working groups (REDD, GEO task, biomass)

– 6 Regional networks (Central/West/East Africa, SE-Asia and Latin america)

Activities & needs: land observation community

1. Global and regional land cover mapping

2. Monitoring and quantifying land change

3. Land cover, biophysical variables and carbon stocks & change

4. Recent drivers of observation progress

Land cover characterization: harmonization and validation

Martin Herold

GOFC-GOLD land cover team

Wageningen University

www.fao.org/gtos/gofc-goldGlobal Observations of Forest Cover and Land Dynamics

Deciduous

ArtificialSnow & Ice

BareHerbaceous

Shrubs

Common land cover classifiers (LCCS)

TreesCover type/ life form

Evergreen

Leaf longevity

Leaf type

Broadleaved

Needle-leaved

Cultivated/managed

Cultivated and managed/

(semi-)natural Aquatic/ flooded

Terrestrial / aquatic+ regularly flooded

Translation

Global land cover datasets

Concept of LCCS land cover classifiers

Thematic standards Reference database (GLC2000)

Comparative validation & assessment

Based on generalized set of eleven LCCS classes

SYNMAP – for carbon cycle modeling

SYNMAP – a global synthesis product of existing global land cover maps to provide a targeted and improved land cover map for carbon cycle modelling purposes; here shown as life form assemblages (Source: M. Jung et al. 2006, Remote Sensing of Environment).

MODIS Collection 5 Land Cover (2001-2008)

Source: M.Friedl, Boston University / NASA

GLOBCOVER (2005/6)

Dataset release: September 2008

GLOBCOVER 2009

GlobCover 2009 – Final Meeting – 9 February 2011, JRC, Italy

An initiative of: In cooperation with:

The most recent and most detailed global land cover map

2009 MERIS data – map released Dec. 2010

Based on the Globcover pre-processing chain

Demonstrates the ability to generate global products on-demand and systematically

Available online for download

50 000 downloads

The land cover map for Russia based on MODIS 250 m dataSergey Bartalev - Russian Academy of Sciences - Space Research Institute

TerraNorte RLC Map for 2010

Needs: approaches to land cover characterization

• Activities moving from independent datasets to synergy products need to continue – international community consensus building

• Datasets can be produced on continuous basis– Support ongoing monitoring projects (data continuity)– Invest in better user interactions and data uptake

• Comparative & operational accuracy assessments: – Synergy and “best” available datasets and information– Regional accuracy numbers – Error propagation and more user-relevant uncertainty analysis

Land cover change assessments

Martin Herold

GOFC-GOLD land cover team

Wageningen University

www.fao.org/gtos/gofc-goldGlobal Observations of Forest Cover and Land Dynamics

Integrated land cover observations

From Herold et al 2008, IEEE Systems

Effort

for f

requen

t

update

Thematic detail

Sp

atia

l d

etai

l

high

highlow

Assuming observation continuity and consistency

IN-SITU (+ IKONOS type)periodically (usually 1-10 yrs)

Detailed physiognomyFloristics and species distributionLand use: i.e. crop type/rotationCalibration and validation

high

LANDSAT/SPOT – typeinter-annual (1-5 yrs)

Vegetation physiognomy

Land change processes

Land type/Phenology

MO

DIS/M

ERIS

(intra

-)ann

ual

p

atte

rnLo

ng-te

rm

tren

ds

Global trends in vegetation dynamics 1981-2006 (AVHRR)

Credit: R. De Jong WU/CGI, Remote sensing of Environment, 2011

Percent gross forest cover loss 2000–2005

per 20x20 km sample block, Hansen et al., 2010, PNAS

Global active fire observations

• Animated figure!

http://modis-fire.umd.edu/MCD45A1.aspContact: Luigi Boschetti <luigi@hermes.geog.umd.edu>

EXAMPLE APPLICATIONS

•1 year of composite of MODIS burned areas, superimposed on surface reflectance to provide geographic context.

•Burned area statistics for the same period, for vegetation type

Africa

0.00E+00

1.00E+05

2.00E+05

3.00E+05

4.00E+05

5.00E+05

6.00E+05

fire

aff

ecte

d a

rea

[km

^2]

0%

5%

10%

15%

20%

25%

30%

un

map

ped

[%

]

croplands

barren_or_sparsely_vegetated

grasslands

savannas

woody_savannas

open_shrublands

closed_shrublands

mixed_forests

deciduous_broadleaf_forest

deciduous_needleleaf_forest

evergreen_broadleaf_forest

evergreen_needleleaf_forest

other

unmapped BA

Needs: approaches to land change characterization

• Continuity and consistency of observations• Need to fully explore and (re-)process archives• Assessing the complexity of land dynamics:

– Seasonality, trends (non-monotonic), fire, disturbances and land use change

– Address the limitations and potentials of satellite-based land change observations

• Need for fine scale data to quantify change

FAO FRA 2010 –remote sensing survey

~ 13,500 monitoring

sites

Towards carbon stocks and change

(i.e. GOFC-GOLD Biomass WG)

Martin Herold

GOFC-GOLD land cover team

Wageningen University

www.fao.org/gtos/gofc-goldwww.gofc-gold.uni-jena.de Global Observations of Forest Cover and Land Dynamics

Large area biomass mapping

Source:Alessandro BacciniWoods Hole Research Center

Source:Valerio AvitabileWUR/FSU Jena

Validation for Uganda

First estimates of C emissions for South America

TREES-II(2004)

TREES-3(2011)

1990-2000 441 427

2000-2005 518

Annual C emissions (Million t C per year)

(C committed over 10 years from 1 year deforestationRepresenting loss of 69% biomass)

Source: Eva, Beuchle et al. in prep.

Contribution of CO2 emissions from deforestation and forest degradation

CO2 emissions from deforestation and forest degradation for 1997-2004: ~ 1.2 Pg C yr–1

(12% [6–17%] of total anthropogenic CO2 emissions)

Peat land emissions: ~ 0.30 Pg C yr–1

(Deforestation + peatland emissions = 15% [8–20%] of total CO2 emissions)

Source: van der Werf et al, 2009, Nature BiogeoSciences

JRC estimate (2002, 2004):

1.1 Pg C yr–1 for 1990s

DeFries et al estimate (2002)

0.9 Pg C yr–1 for 1990s

Needs: land and carbon change characterization

• Global and regional biophysical parameters products exist with varying understanding of uncertainty

• Synergy and consistency among land cover and biophysical information (i.e. biomass, LAI, fAPAR) is required

• Integrated use of land change/activity data:– Link to carbon stock change assessments– Reduce uncertainty in policy relevant estimates – Address the limitations and potentials of satellite-based land

change observations

Further areas of active progress

ECV and REDD

Martin Herold

GOFC-GOLD land cover team

www.fao.org/gtos/gofc-goldwww.gofc-gold.uni-jena.de Global Observations of Forest Cover and Land Dynamics

Land_Cover_cci – KO Meeting – Louvain-la-Neuve 24-25 August 2010

Land Cover Climate Change Initiative

• Driven by GCOS requirements and climate user needs

• Detailed climate user survey (several user groups) and existing global land cover users

• 3 main ways land cover observations/data are used:1. As proxy for a suite of land surface parameters that are assigned

based on PFTs

2. As proxy for human activities in terms natural versus anthropogenic and tracking human activities, i.e. land use affecting land cover (land cover change as driver of climate change)

3. As datasets for validation of model outcomes (i.e. time series) or to study feedback effects (land cover change as consequence of climate change)

Hibbard et al., 2010, Int. J. Climatol.

Increasing overlap and synergies among climate science communities

Variability in capacities for REDD+ monitoring

Consideration of factors:1.Requirements for monitoring forest carbon on national level (IPCC GPG)2.Existing national capacities for national forest monitoring3.Progress in national GHG inventory and engagement in REDD4.REDD particular characteristics: importance of forest fires, soil carbon, deforestation rate etc.5.Specific technical challenges (remote sensing): cloud cover, seasonality, topography, remote sensing data availability and access procedures

Capacity gap:

Capacity gap

Source: Herold, 2009 http://princes.3cdn.net/8453c17981d0ae3cc8_q0m6vsqxd.pdf

Closing remarks

• Essential Climate Variables (ECV) and REDD (post-Kyoto) as key observation drivers

• Consistency, continuity and access to observations is a key requirement for all observation scales– Archives and future satellite missions and in-situ

• International efforts are need to derived transparent, agreed data and estimates

• Monitoring the complexity of land changes• Land cover and change linking to carbon dynamics

is essential and requires further improvements• Validation, stability and uncertainty estimates

– including change and biophysical variables

Some documents

• Essential Climate Variable (ECV) report on standards for observation and reporting:– http://www.fao.org/gtos/doc/ECVs/T09

• GOFC-GOLD REDD Sourcebook:– www.gofc-gold.uni-jena.de/redd

• Translation report for major global and regional land cover legends in LCCS (GOFC-GOLD 43):– http://nofc.cfs.nrcan.gc.ca/gofc-gold/Report%20Series/GOLD_43.pdf

• IPCC background paper on use of remote sensing in LULUCF sector (GOFC-GOLD 33): – http://www.fao.org/gtos/gofc-gold/series.html