Tropentag, September 16 - 18, 2026, Göttingen "Towards multi-functional agro-ecosystems promoting climate-resilient futures"

Accuracy constraints of gedi-based biomass models in heterogeneous coffee agroforestry

¹Bern University of Applied Sciences, HAFL, Austria
²ETH Zurich, Photogrammetry and Remote Sensing
³Eduardo Mondlane University, Department of Forestry
⁴Bern University of Applied Sciences (BFH), School of Agricultural, Forest and Food Sciences (HAFL), Switzerland

Abstract

Greater pressure on African forests has meant that they are now a source,
rather than a carbon sink, driving an urgent need for land-use systems that
restore biomass. While coffee agroforestry is one means to promote carbon
sequestration, its quantification in smallholder systems remains highly
uncertain. A systematic literature review conducted for this study indicated
that global remote sensing models applied to heterogeneous landscapes
frequently suffer from range compression, characterised by signal
saturation and scale mismatches.
We evaluated the suitability of a remote sensing-derived aboveground
biomass (AGB) product for monitoring, reporting, and verification (MRV) in
coffee-based agroforestry on Mount Gorongosa, central Mozambique. Field
data were collected from 36 circular 400 m² plots in smallholder Arabica
coffee agroforests established since 2014, spanning a range of stand ages
and shade-tree compositions. Field AGB was estimated from woody stem
measurements using species-specific, regional and pantropical allometric
equations, then scaled to Mg ha⁻¹ at the plot level. Remote-sensing AGB was
derived from a model trained on GEDI LiDAR AGB footprints and multisource
inputs (Sentinel^-2, ALOS^-2 PALSAR, Digital Elevation Model, land cover map)
from outside of the study area.
The empirical results confirmed the structural biases identified in the
literature. The model showed strong structural bias and poor agreement
with field AGB: mean bias was 16.96 Mg ha⁻¹, RMSE 52.86 Mg ha⁻¹ and
relative RMSE 91.22%. The regression slope (0.253) and high intercept
(60.23 Mg ha⁻¹) revealed systematic overestimation at low biomass and
underestimation at high biomass.
These findings illustrate a critical calibration gap where current GEDI-based
products fail to capture the structural complexity of smallholder systems.
Locally grounded calibration data remain essential for credible AGB
accounting in these heterogeneous landscapes.

Keywords: Aboveground Biomass, Carbon sequestration, Miombo, Mozambique, Remote sensing, Smallholder farming

Contact Address: Johannes Raher, Bern University of Applied Sciences, HAFL, Löfflerweg 11, 6020 Innsbruck, Austria, e-mail: johannes.raherposteo.de