About

We aim to harness the big data and the AI revolution to develop innovative technological approaches to assess nature recovery in both fine detail and at large spatial scales.

We are in the midst of an exponential proliferation of data about our environment from sources as varied as a new generation of satellite sensors, social media posts and time-lapse cameras with image recognition. In concert with this data richness, there is immense potential to use AI/machine learning to fuse, interpret and correlate data to work at both fine spatial resolution and large spatial scale in the midst of significant complexity.

For the first time we have the potential to map and model ecological connectivity across whole countries, map different farming approaches or infrastructure in fine detail and track the connectivity of biodiversity associated with different farming landscapes. We will advance state-of-the-art AI approaches to combine different sources of data, including drones, satellite, survey data and social media, that are robust to a range of environmental scenarios, data noise and model reliability.

We will initially focus on three machine learning tasks to address the information requirements of our programmes:

  • The exploitation of existing machine learning technology and subsequent identification and filling of methodological gaps
  • Novel methods for data interpretation
  • Requirements for hardware, imagery and human intervention for cost-efficient, scalable data analytics.

Projects

Theme outputs

    Kumeh, E.M (2024). The political ecology of cocoa agroforestry and implications for equitable land use in rural Ghana.. Agroforest Syst.

    The relevance of cocoa agroforestry is widely discussed in debates on sustainability transition in cocoa, especially in the context of ending hunger and poverty among cocoa farmers. Whereas this has led to multiple cocoa agroforestry investments by NGOs, governments, and cocoa and chocolate companies in West and Central Africa, a notable gap exists in the literature on how these interventions respond to the needs of cocoa farmers who are typically framed as the primary target of equity in cocoa sustainability discussions. This paper contributes to bridging this gap by analyzing equity in implementing various cocoa agroforestry projects by different actors in Ghana’s Juabeso-Bia Landscape (JBL)

    Publications
    LCNR supported
    • Remote sensing
    • Scale and Technology
    • Society

    Zhang-Zheng H; Adu-Bredu S; Duah-Gyamfi A; Moore S; Addo-Danso SD; Amissah L; Valentini R; Djagbletey G; Anim-Adjei K; Quansah J (2024). Contrasting carbon cycle along tropical forest aridity gradients in West Africa and Amazonia.. Nature communications.

    Tropical forests cover large areas of equatorial Africa and play a substantial role in the global carbon cycle. However, there has been a lack of biometric measurements to understand the forests’ gross and net primary productivity (GPP, NPP) and their allocation. Here we present a detailed field assessment of the carbon budget of multiple forest sites in Africa, by monitoring 14 one-hectare plots along an aridity gradient in Ghana, West Africa. When compared with an equivalent aridity gradient in Amazonia, the studied West African forests generally had higher productivity and lower carbon use efficiency (CUE).

    Publications
    LCNR supported
    • Society
    • Scale and Technology
    • Remote sensing

    Kumeh, Eric Mensah. (2023). How agroecology can help build dynamic cocoa agroforests in Ghana.. Agroforestry at work .

    This article describes the transformative potential of agroecology as a beacon of hope for reestablishing balance in Ghana’s cocoa-forest mosaic landscapes. Agroecology — rooted in the principles of ecological harmony and sustainable agriculture — offers a way to revive and restore biodiversity, empower farmers and ensure a resilient and thriving future for cocoa farms.

    Publications
    LCNR supported
    • Society
    • Remote sensing
    • Scale and Technology
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