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

santoro@gamma-rs.ch

Data release