CCI Biomass 1 st User Workshop, Paris, 25 Sept. 2018 ESA’s GlobBiomass project and datasets Maurizio Santoro Gamma Remote Sensing On behalf of GlobBiomass project team
CCI Biomass 1st User Workshop,Paris, 25 Sept. 2018
ESA’s GlobBiomass project and datasets
Maurizio Santoro
Gamma Remote Sensing
On behalf of GlobBiomass project team
What is GlobBiomass?
● GlobBiomass (2015-2017) was an ESA-funded project, part of the Data User Element (DUE).
The DUE has the aim of favoring the establishment of a long-term relationship between the
User communities and Earth Observation.
● The main purpose of GlobBiomass was to better characterise and to reduce uncertainties of
AGB estimates by developing innovative mapping approaches using EO and in-situ data
○ in five regional sites for the epochs 2005, 2010 and 2015 and
○ for one global map for the year 2010
Why a global map of forest biomass?
● Datasets available based on remote sensing○ Global AGB: Kindermann et al., 2014; GEO-CARBON, 2014; Liu et al., 2015; Hu et al., 2016
○ Biome AGB: Saatchi et al., 2011; Baccini et al., 2012; Thurner et. al., 2014; Avitabile et al.,
2016
● Most datasets use data from around year 2000 or represent AGB at coarse resolution
● Cross-comparisons reveal divergent estimates at local scale
● Errors and uncertainties often not (fully) described
● Weaknesses:○ Handful of remote sensing datasets used, often sub-optimal to derive biomass
○ Strong requirement on reference data for training retrieval models
Data and methods: issues and proposed solutions
● Issue 1: EO does not quantify biomass → The signals of EO data available for 2010 are only
weakly affected by biomass-related forest attributes
● Issue 2: wealth of models relating EO signals to “biomass” → classical approach to retrieve
biomass: train a model with in situ data or surrogate data → unrealistic approach at global
scale to capture spatial variability of the EO signal correctly
● Solution 1: use EO data to exploit as much as possible the information content on “biomass”
● Solution 2: (i) select a well-known modelling framework, (ii) that allows tuning of the model
parameters in space and time, and (iii) does not require in situ data for training (self-calibration
of model)
The GlobBiomass global retrieval method (EO2GSV)
The GlobBiomass global retrieval method (GSV2AGB)
Examples of Water Cloud Model
Boreal: GSV 300 m3/ha @ AGB: 150 Mg/ha (BCEF @ 0.5)
Wet tropics: GSV 300 m3/ha @ AGB: 250 Mg/ha (BCEF @ 0.85)
Envisat ASAR, HH or VV-pol(largest dynamic range)
ALOS PALSAR, HV-pol
Forest aboveground biomass, AGB (Mg/ha) @ 100m
Color bar constrained to 0 – 350 Mg/ha to enhance contrast
Examples of AGB estimates (Mg/ha)
North Poland Riau, Sumatra
Known caveats of AGB estimates Data processing issues → uncompensated topography in ALOS mosaic
West Sumatra DRC
Known caveats of AGB estimates Signal-related issues → Biomass of dense mangroves often underestimated
Matang, Malaysia
Known caveats of AGB estimates Signal-related issues → Biomass of flooded vegetation overestimated
Along Congo River, DRC
AGB standard error (%) @100m
Color bar constrained to 0 – 100% to enhance contrast
Contribution to standard errorTAr = Tropical rainforestTAwa = Tropical moist dec. forestTAwb = Tropical dry forestTBSh = Tropical shrublandTBWh = Tropical desertTM = Tropical mountain
SCf = Subtropical humidSCs = Subtropical drySBSh = Subtropical steppeSBWh = Subtropical desertSM = Subtropical mountain
TeDo = Temperate oceanicTeDc = Temperate continentalTeBSk = Temperate steppeTeBWk = Temperate desertTeM = Temperate mountain
Ba = Boreal coniferousBb = Boeal tundra woodlandBM= Boreal mountainP = Polar
Validation protocolInventory plots
Plot vs. pixel 0.1 deg averages of plots and pixels
Regional statistics
Total volume and above-ground biomass for 2010 Total volume in forest Average GSV in forestGlobBiomass: 694.6 109 m3 GlobBiomass: 142.7 m3/haFAO FRA 2010: 495.6 109 m3 (*) FAO FRA 2010: 121.8 m3/ha
Total above-ground biomass in forest Average AGB in forestGlobBiomass: 522.6 Pg GlobBiomass: 107.3 Mg/haFAO FRA 2010: 469.4 Pg (**) FAO FRA 2010: 115.4 Mg/ha
Forest areaGlobBiomass (based on CCI Land Cover): 4.87 109 haFAO FRA 2010: 4.06 109 ha
No data in FAO FRA 2010 for major countries:(*) Australia, Dominica, Ecuador, El Salvador, Paraguay, Togo, Venezuela (**) Dominica, Ecuador, El Salvador, Paraguay, Togo, Uruguay, Venezuela
Comparison with FAO FRA 2010 AGB statisticsAfrica: Countries adopting BCEF > 2
Asia:Right: Countries with topographyLeft: SE Asian countries
Europe: Forest fragmentation
Central America: countries adopting BCEF > 1.5
South America: Guyana, Fr. Guyana and Suriname, different FRA values
OceaniaPNG based on lowland dataNZ based on commercial forestNote: Size of dot proportional to forest area
Pakistan
Argentina
Ivory Coast
Cuba
PNG
New Zealand
Comparison with EO-based AGB estimates
• GlobBiomass generated the first global dataset of forest biomass at moderate resolution
• RS does not „see“ biomass → combination of available data streams mandatory to limit
estimation errors
• Strong confidence on the spatial distribution of biomass and its levels globally
• New set of estimates that may impact the global carbon budget so far assumed
• The estimates have local systematic errors BUT we understand these errors ○ EO data sub-optimal to estimate biomass
○ Ready-to-use EO data products often only choice, not the best one though
○ One global model, strongly adaptive, achieved a fairly decent result but we could not avoid
local over/underestimation due to the simplicity of the inversion model
Conclusions
• We need multiple sources of EO data that senses structure, species and moisture are
envisaged à currently, these are not available
• We need EO data as clean as possible from errors
• We need to explore the EO signals to understand how to “best” set up retrieval models
• Biomass retrieval models need in situ data for development but not necessarily for
operations
• We need to explore the impact of scales (remote sensing vs. in situ) in what we see
• We need a solid statistical framework for accounting for errors and uncertainties
• We need to move from a single epoch to a sequence of maps
A perspective from a “data producer”
• GlobBiomass global data products of AGB and GSV @ 100 m (version of 2018-05-31)
available at http://globbiomass.org/products/global-mapping/
• Cite as: Santoro, M. (2018): GlobBiomass – global datasets of forest biomass. PANGAEA,
https://doi.pangaea.de/10.1594/PANGAEA.894711
• For questions, comments and issues, please refer to
Maurizio Santoro
Data release