Conference Agenda
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Daily Overview |
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Biomass First Results IV Location: Red Hall Session Chair: Konstantinos Papathanassiou, German Aerospace Center, DLR e.V. Session Chair: Klaus Scipal, European Space Agency | |
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11:50am - 12:10pm
Overview of the BIOMASS Level 3 product processor 1: ISAE-SUPAERO & CESBIO, France; 2: ESA ESRIN, Italy; 3: ARESYS, Italy; 4: RHEA, Italy; 5: Private; 6: ESA ESTEC, The Netherlands This contribution describes the principles and implementation of a Level 3 (L3) product processor for ESA’s BIOMASS mission. This mission aims at reducing the uncertainty in the worldwide spatial distribution and dynamics of forest biomass, and will achieve this objective using a P-band SAR, providing global maps of forest biomass stocks, forest disturbance and growth [1]. In its dual-baseline interferometric operating phase, the BIOMASS mission will provide at each Global Cycle (GC), i.e. approximately every 7 months, a set of world-wide Level 2b (L2b) products consisting of maps of the Above Ground Biomass (AGB), Forest Height (FH) and Forest Disturbance (FD). The L2b AGB and FH estimation processes being led independently, and at rather local spatial and temporal scales, it is very likely that output maps show some variability, related to the intrinsic uncertainty of L2b estimators, but also to potential exceptionally unfavorable factors, such as severe meteorological conditions (rain, wind), problematic propagation effects, or non-optimal baseline configuration. The objective of L3 processing is to improve the consistency of L2b maps, by enforcing geophysical constraints through an iterative statistical regularization process. Three kinds of constraints are considered: - spatial consistency is derived by comparing the spatial statistics of each product with autocorrelation function features computed over a wider neighborhood. Significantly different behaviors are to be penalized in order to guarantee spatially homogeneous estimates over undisturbed areas. - temporal consistency is evaluated by observing estimates performed for different GCs, and by limiting the positive change rate (gain velocity) of AGB and FH parameters. One may note maximal gain rates are fixed according the considered geographical location and to the observed type of forest. Unlike gains, AGB and FH losses are not constrained, as they can happen in a very abrupt way. - allometric consistency allows to mutually regularize AGB and FH fields using local, and forest-class specific relationships [2]. Allometric equations, as well as their associated dispersion, are directly estimated from L2b maps, at each GC, for each of the forest class provided by a land-cover map, and at the scale of an L2b tile, i.e. over regions of about 100 km x 100 km at the equator. The regularization process is implemented under the form a Maximum Likelihood optimization, aiming to determine AGB and FH space-time maps which maximize a compound likelihood function, composed of losses terms related to L2b, spatial, temporal and allometric statistics [3]. A log-normal framework is adopted, which allows to represent this optimization as a very large, but sparse, system of linear equations. At each new GC, the optimization process is run considering the freshly estimated L2b parameters in addition to the previously regularized fields. As a consequence, L3 estimate maps are expected to change significantly during the lifetime of the mission, with a quality level that increases with time. The L3 processor delivers, at each GC and for each processed tile, L3 AGB and FH maps together with their level of confidence [4] and descriptors of their temporal evolution, auxiliary information related to the considered land-cover maps, and parameters of the BIOMASS allometric relationships at all dates and for all forest types. An example of regularization, built from realistic AGB and FH maps estimated over Gabon, is provided and illustrates the capabilities of the processor to actually improve the consistency of L2b product maps. [1] Quegan, S. et al. “The European Space Agency BIOMASS mission: Measuring forest above-ground biomass from space”, Remote Sensing of Environment, Volume 227, 2019, Pages 44-60, ISSN 0034-4257, https://doi.org/10.1016/j.rse.2019.03.032. [2] Chave, J. et al. “Improved allometric models to estimate the aboveground biomass of tropical trees”, Global change biology, 2014, 20(10), pp.3177-3190. [3] Tarantola, A. (2005) Inverse Problem Theory and Methods for Model Parameter Estimation. SIAM: Society for Industrial and Applied Mathematics, 342 p. https://doi.org/10.1137/1.9780898717921 [4] Keener, R.W., 2010. Theoretical statistics: Topics for a core course. Springer Science & Business Media. | |
