Transcript “Global and regional land cover and land change monitoring: progress and needs” Martin Herold Wageningen University ([email protected]) www.fao.org/gtos/gofc-gold Global Observations of Forest Cover and Land Dynamics.

“Global and regional land cover and
land change monitoring:
progress and needs”
Martin Herold
Wageningen University
([email protected])
www.fao.org/gtos/gofc-gold
Global 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-gold
Global Observations of Forest Cover and Land Dynamics
Concept of LCCS land cover classifiers
Common land cover
classifiers (LCCS)
Global land cover
datasets
Cover type/ life form
Trees
Shrubs
Herbaceous
Bare
Snow & Ice
Artificial
Translation
Leaf longevity
Leaf type
Evergreen
Needle-leaved
Broadleaved
Cultivated and managed/
(semi-)natural
Cultivated/
managed
Deciduous
Terrestrial / aquatic+
regularly flooded
Aquatic/
flooded
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
 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 ondemand and systematically
 Available online for
download
An initiative of:
GlobCover 2009 – Final Meeting – 9 February 2011, JRC, Italy
In cooperation
with:
TerraNorte RLC Map for 2010
The land cover map for Russia based on MODIS 250 m data
Sergey Bartalev - Russian Academy of Sciences - Space Research Institute
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-gold
Global Observations of Forest Cover and Land Dynamics
Integrated land cover observations
high
IN-SITU (+ IKONOS type)
periodically (usually 1-10 yrs)
Spatial detail
Assuming observation
continuity and
consistency
Detailed physiognomy
Floristics and species distribution
Land use: i.e. crop type/rotation
Calibration and validation
LANDSAT/SPOT – type
inter-annual (1-5 yrs)
Vegetation physiognomy
Land change
processes
Land type/
Phenology
low
From Herold et al 2008, IEEE Systems
Thematic detail
high
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,
Global active fire observations
• Animated figure!
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
6.00E+05
30%
5.00E+05
25%
4.00E+05
20%
3.00E+05
15%
2.00E+05
10%
1.00E+05
5%
0.00E+00
l-
Ju
01
unmapped [%]
fire affected area [km^2]
Africa
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
0%
BA
Au
01
g-
BA
Se
01
p-
BA
O
ct
01
BA
N
o
01
v-
BA
D
e
01
c-
BA
Ja
02
n-
BA
b-
Fe
02
BA
ar
M
02
BA
rAp
02
BA
BA
2
2
-0
-0
ay
un
J
M
BA
http://modis-fire.umd.edu/MCD45A1.asp
Contact: Luigi Boschetti <[email protected]>
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 satellitebased 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-gold
www.gofc-gold.uni-jena.de
Global Observations of Forest Cover and Land Dynamics
Large area biomass mapping
Source:
Alessandro Baccini
Woods Hole Research Center
Validation for Uganda
Source:
Valerio Avitabile
WUR/FSU Jena
First estimates of C emissions for South America
Annual C emissions (Million t C per year)
1990-2000
2000-2005
TREES-II
(2004)
TREES-3
(2011)
441
427
518
(C committed over 10 years from 1 year deforestation
Representing loss of 69% biomass)
Source: Eva, Beuchle et al. in prep.
Contribution of CO2 emissions
from deforestation and forest degradation
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
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
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 satellitebased land change observations
Further areas of active progress
ECV and REDD
Martin Herold
GOFC-GOLD land cover team
www.fao.org/gtos/gofc-gold
www.gofc-gold.uni-jena.de
Global Observations of Forest Cover and Land Dynamics
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)
Land_Cover_cci – KO Meeting – Louvain-la-Neuve 24-25 August 2010
Increasing
overlap and
synergies
among climate
science
communities
Hibbard et al., 2010,
Int. J. Climatol.
Variability in capacities for REDD+ monitoring
Capacity gap
Capacity gap:
Consideration of factors:
1. Requirements for monitoring forest carbon on national level (IPCC GPG)
2. Existing national capacities for national forest monitoring
3. Progress in national GHG inventory and engagement in REDD
4. 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
Source: Herold, 2009 http://princes.3cdn.net/8453c17981d0ae3cc8_q0m6vsqxd.pdf
Closing remarks
• Essential Climate Variables (ECV) and REDD (postKyoto) 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