Description

Estimating aboveground biomass is essential for generating vegetation inventories and understanding carbon cycles. In this sense, it constitutes an important tool for supporting policies aimed on mitigating climate changes. Several active sensors, among them the GEDI, ICESat, TanDEM-X and NISAR missions, combined with optical sensors provide synergistic data with different and complementary information about aboveground biomass. This MSc topic focus on developing a Deep Learning method for estimating forests biomass based on data from multiple sensors.

Objectives and Methodology

The student will initially revise the literature on biomass estimation and on multimodal deep learning models. The main focus will be on developing an end-to-end trainable multimodal/multi-path deep neural network composed of multiple paths or networks, optimized by a combined loss function, and able to combine LiDAR, SAR and optical imagery for estimating aboveground biomass maps, especially for forest areas.

Further reading

[1] Qi, W., Saarela, S., Armston, J., Ståhl, G., & Dubayah, R. (2019). Forest biomass estimation over three distinct forest types using TanDEM-X InSAR data and simulated GEDI lidar data. Remote Sensing of Environment, 232, 111283.
[2] Saarela, S.; Holm, S.; Healey, S.P.; Andersen, H.-E.; Petersson, H.; Prentius, W.; Patterson, P.L.; Næsset, E.; Gregoire, T.G.; Ståhl, G. Generalized Hierarchical Model-Based Estimation for Aboveground Biomass Assessment Using GEDI and Landsat Data. Remote Sens. 2018, 10, 1832.
[3] Silva, C. A., Duncanson, L., Hancock, S., Neuenschwander, A., Thomas, N., Hofton, M., ... & Dubayah, R. (2021). Fusing simulated GEDI, ICESat-2 and NISAR data for regional aboveground biomass mapping. Remote Sensing of Environment, 253, 112234.