Analysing forest aboveground carbon dynamics in the Amazonia forests using dense time-series of satellite data and artificial intelligence

Overview:
Amazon forests hold the largest pools of forest carbon, but this estimate remains poorly quantified (Espirito-Santo et al. 2014). The remote sensing methods adopted to estimate the spatial variation of above-ground biomass in tropical forests, notably the Brazilian Amazon, are usually scarce and often limited in their spatial distribution. There are notable differences among current biomass maps (Figure 1), leading to high uncertainties in the carbon emissions calculated from land-use changes when referring to these specific biomass maps. Therefore, we urge a new approach to estimate forest biomass and its changes, without heavy reliance on forest plot data.
Recent advances in cloud and high-performance computing are paralleled with advanced artificial intelligence (AI) algorithms and significant investment in new satellite missions. Machine learning (Le Cun et al. 2015) have previously been applied to hyperspectral image classification (Hu et al. 2015), CORINE land cover mapping from Sentinel-1 SAR images (Balzter et al. 2015) and forest biomass mapping using a combination or SAR and optical images (Rodriguez-Veiga et al, 2016). Machine learning enables automatic detection of forest changes of satellite images. The paradigm of looking for spatial and temporal patterns instead of the historic focus on spectral information in satellite imagery allows the identification of the different types of forest dynamics (disturbance and succession). AI can also be used to accurately estimate from space forest biophysical parameters that are difficult to measure in forest inventory data (Rodriguez-Veiga et al, 2017) (Figure 1).
This interdisciplinary studentship aims to explore the use of machine learning to quantify changes of aboveground biomass carbon in dense time-series satellite data of several Brazilian forest sites. Time-series stacks of multispectral optical and synthetic Aperture Radar (SAR) sensors will be input into the AI. The AI will be trained based on measurements collected from in-situ forest inventories and visual interpretation of very high resolution images.
Research questions:
1. What are the carbon stocks and fluxes of aboveground biomass of the Amazon forests?
2. How accurately can an AI be trained to quantify forest dynamics of the Amazon based on satellite time-series information?
3. How accurately can the associated aboveground biomass loss or gain be estimated?


Figure 1: Global and pantropical maps of aboveground biomass (Rodriguez-Veiga, et al., 2017).