Data

The unrealised potential of agroforestry (Processed maps GHA 2022 all exports)

The University of Queensland
Dr Simon Hart (Aggregated by) Dr Wilma J. Blaser Hart (Aggregated by)
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ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Adc&rfr_id=info%3Asid%2FANDS&rft_id=info:doi10.48610/a2ecf22&rft.title=The unrealised potential of agroforestry (Processed maps GHA 2022 all exports)&rft.identifier=RDM ID: abaaca72-92d6-4007-80fa-ac5e577bc070&rft.publisher=The University of Queensland&rft.description=This dataset is part of a larger collection supporting the analysis in Becker et al., Nature Sustainability (2025) on the climate mitigation potential of cocoa agroforestry in West Africa. The full dataset is published via UQ eSpace as a series of interlinked records, each representing a different stage of the research workflow—from raw imagery to processed data products and analysis code. This subset contains processed drone-derived map exports, including orthomosaics, digital surface models (DSM), digital terrain models (DTM), and vegetation height maps. These layers were derived from aerial imagery captured by drones during field surveys conducted across cocoa farms in Ghana in 2022. Vegetation height was calculated by subtracting the DTM from the DSM, providing spatially explicit estimates of canopy height across surveyed farms.&rft.creator=Dr Simon Hart&rft.creator=Dr Wilma J. Blaser Hart&rft.date=2025&rft_rights= https://guides.library.uq.edu.au/deposit-your-data/license-reuse-data-agreement&rft_subject=eng&rft_subject=Sustainable agricultural development&rft_subject=Agriculture, land and farm management&rft_subject=AGRICULTURAL, VETERINARY AND FOOD SCIENCES&rft_subject=Agroforestry&rft_subject=Forestry sciences&rft_subject=Deep learning&rft_subject=Machine learning&rft_subject=INFORMATION AND COMPUTING SCIENCES&rft.type=dataset&rft.language=English Access the data
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School of the Environment

Full description

This dataset is part of a larger collection supporting the analysis in Becker et al., Nature Sustainability (2025) on the climate mitigation potential of cocoa agroforestry in West Africa. The full dataset is published via UQ eSpace as a series of interlinked records, each representing a different stage of the research workflow—from raw imagery to processed data products and analysis code. This subset contains processed drone-derived map exports, including orthomosaics, digital surface models (DSM), digital terrain models (DTM), and vegetation height maps. These layers were derived from aerial imagery captured by drones during field surveys conducted across cocoa farms in Ghana in 2022. Vegetation height was calculated by subtracting the DTM from the DSM, providing spatially explicit estimates of canopy height across surveyed farms.

Issued: 2025

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Subjects
Agricultural, Veterinary and Food Sciences | Agriculture, Land and Farm Management | Agroforestry | Deep Learning | Forestry Sciences | Information and Computing Sciences | Machine Learning | Sustainable Agricultural Development | eng |

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Identifiers
ACN 633 798 857