Scaling up pan-genomics for plant breeding
Delivering a pangenome approach that drastically improves the analytical power on plant data
Recent extreme droughts combined with accelerating human exploitation are pushing tropical forests to the point where they cannot recover, making them vulnerable to large unprecedented wildfires. There is as such an urgent need to monitor the recovery capacity of tropical forests. While time series-based break detection approaches have demonstrated potential to measure tropical forest recovery capacity, they have not yet been applied over large amounts of satellite data. The reasons for this are twofold:
1) These methods require expert knowledge and are very compute intensive 2) They cannot be easily scaled over large amounts of satellite data
This proposal addresses these two critical bottlenecks by:
1) Exploring innovative solutions for automated parameter optimization2) Making use of high-performance and distributed computing to dramatically reduce the practical runtimes for the involved methods
By adapting and combining these innovative technologies, we intend to measure forest recovery capacity at unprecedented spatial and temporal scales using the new RADAR Sentinel-1 satellite data. By making data and algorithms more accessible and scalable, this project addresses an urgent need of Dutch service providers and will demonstrate the potential of European Sentinel satellite data for large-scale monitoring tropical forests.