Deep Learning Based 3D Point Cloud Regression for Estimating Forest Biomass

Robust quantification of forest carbon stocks and their dynamics is important for climate change mitigation and adaptation strategies [FAO and UNEP, 2020]. The Paris Agreement [United Nations / Framework Convention on Climate Change, 2015] and the IPCC [Shukla et al., 2019] acknowledge that climate change mitigation goals cannot be achieved without a substantial contribution from forests. Spatial details in the carbon budget of forests are necessary to encourage transformational actions towards a sustainable forest sector [Harris et al., 2021, 2012]. Currently, many countries do not have nationally specific forest carbon accumulation rates but rather rely on default rates from the IPCC 2018 [Masson-Delmotte et al., 2019, Requena Suarez et al., 2019]), without accounting for finer-scale variations of carbon stocks [Cook-Patton et al., 2020]. Precise spatio-temporal monitoring of forest carbon dynamics at large scales has proven to be challenging [Erb et al., 2018, Griscom et al., 2017]. This is due to the complex structure of forests, topographic features, and land management practices [Tubiello et al., 2021, Lewis et al., 2019]. Technological developments in remote sensing and the concurrent increased availability of field-based measurements have led to an improvement in estimating carbon stocks using remote sensing observations of forest attributes that serve as proxy for above-ground biomass (AGB) [Knapp et al., 2018, Bouvier et al., 2015, Pan et al., 2013]. Currently, three remote sensing techniques are applied to collect data for AGB estimates: i) passive optical imagery, ii) synthetic aperture radar (SAR), and iii) light detection and ranging (LiDAR).