Data

The unrealised potential of agroforestry (Processed maps GHA 2021 point clouds)

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:doi10.48610/53e3189&rft.title=The unrealised potential of agroforestry (Processed maps GHA 2021 point clouds)&rft.identifier=RDM ID: 20e61c82-b785-4552-a7c7-ff5d76d7ced2&rft.publisher=The University of Queensland&rft.description=This dataset is part of a larger collection accompanying the analysis presented in “The unrealized potential of agroforestry for an emissions-intensive agricultural commodity” (Becker et al., Nature Sustainability, 2025). The full dataset has been 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 unclassified point cloud data (LAS format) generated from drone imagery collected across cocoa farms in Ghana in 2021. These point clouds were produced as part of the standard photogrammetric workflow but were not directly used in the published analysis. Nonetheless, they may be valuable for users interested in alternative processing workflows, tree segmentation, or structural vegetation metrics.&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

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This dataset is part of a larger collection accompanying the analysis presented in “The unrealized potential of agroforestry for an emissions-intensive agricultural commodity” (Becker et al., Nature Sustainability, 2025). The full dataset has been 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 unclassified point cloud data (LAS format) generated from drone imagery collected across cocoa farms in Ghana in 2021. These point clouds were produced as part of the standard photogrammetric workflow but were not directly used in the published analysis. Nonetheless, they may be valuable for users interested in alternative processing workflows, tree segmentation, or structural vegetation metrics.

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