ESA s GlobBiomass project and datasets. Maurizio Santoro. Gamma Remote Sensing. On behalf of GlobBiomass project team

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1 ESA s GlobBiomass project and datasets Maurizio Santoro Gamma Remote Sensing On behalf of GlobBiomass project team CCI Biomass 1 st User Workshop, Paris, 25 Sept. 2018

2 What is GlobBiomass? GlobBiomass ( ) 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

3 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

4 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)

5 The GlobBiomass global retrieval method (EO2GSV)

6 The GlobBiomass global retrieval method (GSV2AGB)

7 Examples of Water Cloud Model Envisat ASAR, HH or VV-pol (largest dynamic range) ALOS PALSAR, HV-pol Boreal: GSV 300 m 3 AGB: 150 Mg/ha 0.5) Wet tropics: GSV 300 m 3 AGB: 250 Mg/ha 0.85)

8 Forest aboveground biomass, AGB 100m Color bar constrained to Mg/ha to enhance contrast

9 Examples of AGB estimates (Mg/ha) North Poland Riau, Sumatra

10 Known caveats of AGB estimates Data processing issues uncompensated topography in ALOS mosaic West Sumatra DRC

11 Known caveats of AGB estimates Signal-related issues Biomass of dense mangroves often underestimated Matang, Malaysia

12 Known caveats of AGB estimates Signal-related issues Biomass of flooded vegetation overestimated Along Congo River, DRC

13 AGB standard error Color bar constrained to 0 100% to enhance contrast

14 Contribution to standard error TAr = Tropical rainforest TAwa = Tropical moist dec. forest TAwb = Tropical dry forest TBSh = Tropical shrubland TBWh = Tropical desert TM = Tropical mountain SCf = Subtropical humid SCs = Subtropical dry SBSh = Subtropical steppe SBWh = Subtropical desert SM = Subtropical mountain TeDo = Temperate oceanic TeDc = Temperate continental TeBSk = Temperate steppe TeBWk = Temperate desert TeM = Temperate mountain Ba = Boreal coniferous Bb = Boeal tundra woodland BM= Boreal mountain P = Polar

15 Validation protocol Inventory plots Regional statistics Plot vs. pixel 0.1 deg averages of plots and pixels

16 Total volume and above-ground biomass for 2010 Total volume in forest Average GSV in forest GlobBiomass: m 3 GlobBiomass: m 3 /ha FAO FRA 2010: m 3 (*) FAO FRA 2010: m 3 /ha Total above-ground biomass in forest GlobBiomass: Pg FAO FRA 2010: Pg (**) Average AGB in forest GlobBiomass: Mg/ha FAO FRA 2010: Mg/ha Forest area GlobBiomass (based on CCI Land Cover): ha FAO FRA 2010: 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

17 Comparison with FAO FRA 2010 AGB statistics Africa: Countries adopting BCEF > 2 Ivory Coast Asia: Right: Countries with topography Left: SE Asian countries Pakistan Europe: Forest fragmentation PNG Central America: countries adopting BCEF > 1.5 Cuba Argentina New Zealand South America: Guyana, Fr. Guyana and Suriname, different FRA values Note: Size of dot proportional to forest area Oceania PNG based on lowland data NZ based on commercial forest

18 Comparison with EO-based AGB estimates

19 Conclusions 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

20 A perspective from a data producer 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

21 Data release GlobBiomass global data products of AGB and 100 m (version of ) available at Cite as: Santoro, M. (2018): GlobBiomass global datasets of forest biomass. PANGAEA, For questions, comments and issues, please refer to Maurizio Santoro santoro@gamma-rs.ch