Remote sensing liana infestation in an aseasonal tropical forest: addressing mismatch in spatial units of analyses

Chris J. Chandler (Lead / Corresponding author), Geertje M. F. van der Heijden, Doreen S. Boyd, Mark E. J. Cutler, Hugo Costa, Reuben Nilus, Giles Foody

    Research output: Contribution to journalArticlepeer-review

    8 Citations (Scopus)
    103 Downloads (Pure)


    The ability to accurately assess liana (woody vine) infestation at the landscape level is essential to quantify their impact on carbon dynamics and help inform targeted forest management and conservation action. Remote sensing techniques provide potential solutions for assessing liana infestation at broader spatial scales. However, their use so far has been limited to seasonal forests, where there is a high spectral contrast between lianas and trees. Additionally, the ability to align the spatial units of remotely sensed data with canopy observations of liana infestation requires further attention. We combined airborne hyperspectral and LiDAR data with a neural network machine learning classification to assess the distribution of liana infestation at the landscape‐level across an aseasonal primary forest in Sabah, Malaysia. We tested whether an object‐based classification was more effective at predicting liana infestation when compared to a pixel‐based classification. We found a stronger relationship between predicted and observed liana infestation when using a pixel‐based approach (RMSD = 27.0% ± 0.80) in comparison to an object‐based approach (RMSD = 32.6% ± 4.84). However, there was no significant difference in accuracy for object‐ versus pixel‐based classifications when liana infestation was grouped into three classes; Low [0–30%], Medium [31–69%] and High [70–100%] (McNemar’s χ2 = 0.211, P = 0.65). We demonstrate, for the first time, that remote sensing approaches are effective in accurately assessing liana infestation at a landscape scale in an aseasonal tropical forest. Our results indicate potential limitations in object‐based approaches which require refinement in order to accurately segment imagery across contiguous closed‐canopy forests. We conclude that the decision on whether to use a pixel‐ or object‐based approach may depend on the structure of the forest and the ultimate application of the resulting output. Both approaches will provide a valuable tool to inform effective conservation and forest management.
    Original languageEnglish
    Pages (from-to)397-410
    Number of pages14
    JournalRemote Sensing in Ecology and Conservation
    Issue number3
    Early online date13 Feb 2021
    Publication statusPublished - Sept 2021


    • Hyperspectral imaging
    • LiDAR
    • liana infestation
    • neural network
    • pixel-based soft classification
    • segmentation

    ASJC Scopus subject areas

    • Ecology, Evolution, Behavior and Systematics
    • Ecology
    • Computers in Earth Sciences
    • Nature and Landscape Conservation


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