A Digital Twin Integrating Knowledge and AI for Understanding Carbon and Biodiversity Corridors in Central Africa

Home to the world's second-largest contiguous rainforest, Central Africa is a crucial land carbon reservoir and the major habitat of thousands of endemic species of plants and wildlife. Past decades of land use activities have threatened Central Africa, resulting in the loss of millions of hectares of humid primary forest and fragmented landscapes. Carbon and biodiversity corridors, connecting protected habitats across landscapes, can mitigate the effects of land use and climate change on biodiversity and enhance carbon storage capacity. Identifying and managing these corridors requires scientific understanding and technical tools to assess the current conditions of carbon storage and biodiversity, along with the vulnerability to climate change, land use change, resource exploitation, and wildfires in the future. This project aims to build a digital twin of carbon and biodiversity corridors in Central Africa by integrating knowledge-based models and AI to enable detailed analysis of the current status and future forecasts at high resolution under a broad spectrum of scenarios. To achieve this, several research gaps and challenges need to be addressed. First, most existing high-resolution forest maps derived from remote sensing products focus on the spatial extent of forests but do not accurately reflect their carbon status due to misalignments between optical signals and height structures, leading to inaccurate carbon estimates. Moreover, despite their relevance, 3D forest structure and connectivity have yet to be considered in existing biodiversity intactness assessments and associated conservation priorities. Second, existing mechanistic ecosystem models consider an extensive range of factors for forecasting quality. However, this leads to high computational costs, significantly constraining the forecasting ability at both high resolution and large spatial extents. Furthermore, the ecosystem models, biodiversity, and connectivity models lack connections with each other, despite their strong linkage. Third, the impact assessment of carbon and biodiversity corridors needs to explicitly consider diverse scenarios, including climate change, land use change, and wildfires. It also needs to explore different management plans (e.g., protected area allocation) in response to different scenarios. Such analyses require a highly efficient simulation and optimization framework. To bridge the technical gaps, this project aims to make the following advances to enable the digital twin capabilities: (1) We will develop a four-dimensional approach that combines the two-dimensional geographical space with orbital LiDAR measurements (e.g., GEDI, ICESat-2, AfriSAR) and time (i.e., history of disturbance) to enable a high-fidelity reconstruction of the digital replica of forest structure and aboveground carbon stocks. The new information will be incorporated to develop novel layers of 3D forest structural connectivity and enhance the Biodiversity Intactness Index. (2) We will develop a new computational paradigm of ecosystem modeling by creating a high-accuracy AI-accelerated version of the Ecosystem Demography model using deep learning to realize scalable forecasting at high spatial resolution. We will also enhance data assimilation capabilities with knowledge-guide learning. Additionally, we will integrate carbon and biodiversity modeling in a corridor framework to maximize co-benefits for biodiversity and climate mitigation. (3) To enable impact assessment capabilities, we will further enhance the simulation models' robustness under the wide range of future scenarios including alternate climate assumptions from CMIP6. Furthermore, we will develop an optimization framework that integrates multiple dimensions (e.g., climate, land use change, wildfire) to inform management decisions, including protected area prioritization and allocation.

Expand current definitions of modeling and leverage state-of-the-art computer and information science for innovating advanced modeling techniques as well as new technologies and frameworks that will be essential in the development of Earth System Digital Twins

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