Using New Earth Observation and Computational Approaches to Map Dry Forests and their Carbon Dynamics Across the Tropics (SENSE CDT)

Summary: The global extent and distribution of tropical dry forests versus savannas is poorly known. The project will use new data from recently launched satellites and the latest computational approaches to map dry forests versus savannas across the globe. The student will then estimate their carbon storage and its change over the last 15 years.

Background and Motivation: Earth observation approaches that aim to map the distribution and carbon storage of forests are generally successful for tropical moist forests, but have been less so for tropical dry forests. Tropical dry forests are shorter in stature and may not have a closed canopy, and are thus frequently confused with wooded savannas in remotely sensed imagery and products (Beuchle et al. 2015). Yet, knowing the distribution and relative extent of tropical dry forests versus savannas is imperative to understanding the carbon cycle of the dry tropics and to develop appropriate management strategies for tropical vegetation (Dexter et al. 2018). Tropical dry forests store more above-ground carbon than savannas, while fire is damaging to tropical dry forests and their carbon storage ability. In contrast, fire is necessary to the ecosystem function and biodiversity of savannas. This project will improve ecosystem management in the dry tropics and determine the role of the dry tropics in global carbon cycles.

Key Research Questions:

Q1) What remotely sensed information (e.g. phenology, fire occurrence, canopy height) best predicts the occurrence of tropical dry forests versus savannas?

Q2) Where do tropical dry forests versus savannas occur across the globe, and what are the major environmental and anthropogenic drivers of their distribution?

Q3) How much carbon is stored in dry forests versus savannas, and how has this changed from the mid-2000s to 2020, due either to land conversion or changing tree density?

Methodology: Cloud-based analysis of new remote sensing products will allow us to better distinguish tropical dry forests from savannas, and to successfully map the distribution of dry forest across the tropics (Q1, Q2, Q3). The proposed project will leverage optical satellite imagery and phenology measures, such as NDVI and EVI, from the Sentinel-2 satellite, fire products from MODIS and data from the Global Ecosystem Dynamics Investigation (GEDI) and other spaceborne LiDAR instruments to distinguish tropical dry forests from savanna (Q1, Q2). The latter data products will be used to map maximum canopy height and percent canopy cover in 20m pixels across the dry tropics, which will in turn allow estimation of above-ground biomass and its change from the mid-2000s to the present (Q3). The project will depend on ground-truthed mapping of tropical dry forests versus savannas (Miranda, Dexter et al. 2018) (Q1), and a dataset of canopy height, tree density and above-ground biomass from >2000 plots in tropical dry forests and savannas that is being assembled by the supervisors as part of a recently funded NERC Large Grant (Q3). Artificial intelligence (AI) and machine learning approaches will be used to build statistical models that relate ground-truthed information to EO data and enable wall-to-wall mapping (Q1,Q2). The latter will rely on expertise from 2nd supervisor Armston.

This PhD is part of the NERC and UK Space Agency funded Centre for Doctoral Training "SENSE": the Centre for Satellite Data in Environmental Science. SENSE will train 50 PhD students to tackle cross-disciplinary environmental problems by applying the latest data science techniques to satellite data. All our students will receive extensive training on satellite data and AI/Machine Learning, as well as attending a field course on drones, and residential courses hosted by the Satellite Applications Catapult (Harwell), and ESA (Rome). All students will experience extensive training on professional skills, including spending 3 months on an industry placement. See http://www.eo-cdt.org