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Speaker: Alejandro Coca-Castro
Abstract: Despite extensive efforts in modelling and monitoring forest cover and forest change, few advances in the study of land cover (LC) and land use (LU) dynamics following deforestation (so-called post-loss LC/LU change) exist. The current plethora of multi-source datasets with considerable high-frequency records (e.g. earth observation data) and emerging technologies (e.g. cloud computing) to process them, provide new possibilities in the study of long-term (>2 years) LC/LU dynamics over deforested areas. Building on these advances, this presentation aims to share the main findings and learnings of a 4-year PhD research involving the development and assessment of models and tools for mapping and analysing post-loss LC/LU dynamics pantropically.
The presentation is divided into four sections covering pertinent information related to the study of post-loss LC/LU change: i) a state-of-art review exploring the theory, definitions, previous work and datasets, ii) a local-scale analysis over 120 deforested sites across the Peruvian Amazon, iii) large-scale generation of multitemporal spatially-explicit land cover information from 20-year MODIS satellite data by using a deep recurrent neural network architecture and cloud-computing technologies, and iv) discovery and explanation of patterns using the generated spatially-explicit dataset across 2004-2018 deforested areas over the Amazon region and potentials impacts on key ecosystem services.
This research supports modellers of ecosystem services, biodiversity, and other deforestation-relevant topics by going beyond the immediate state of deforestation to understanding the typologies and dynamics of long-term post-loss LC/LU change trajectories, which will have impacts on these areas of research.
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About the speaker
Alejandro Coca Castro is a PhD student in the Department of Geography. He has a MSc in Environmental Monitoring, Modelling and Management from King’s and is affiliated as Visiting Researcher at the International Center for Tropical Agriculture (CIAT).
Before joining King's, Alejandro was involved in several projects supported by Geographic Information Systems (GIS) and remote sensing for decision making in forest, land and climate policy at multiple scales. Alejandro is confident in a variety of GIS and data analytics tools with particular interest in applied research based on novel data-driven approaches, and the automation of complex and large data tasks using cutting edge-technologies (eg big data and cloud-computing).