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Inferring forest stand structure from LiDAR remote sensing data

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Forest information at the level of the stand is crucial for deriving reliable estimates of the capacity of the future carbon sink, yet the data are restricted to a small subset of the forest. Collection of the required datasets can be labour-intensive and costly and thus data availability is restricted. Our aim is to develop a model that allows forest stand structure to be derived from LiDAR remote sensing data; this therefore offers a tool for predicting inventory information across large scales, from which current carbon stocks can be measured and future stocks predicted.

We have developed a model for predicting the distribution of LiDAR first returns retrieved from stand-level data, demonstrating that exposed, accumulated and overlapping crown area at a given height are all critical factors in determining where a return is recorded. This model predicts to a high degree of accuracy which has allowed it to be effectively implemented in the extraction of stem diameter distributions from LiDAR return patterns across whole landscapes.

This talk is part of the Plant Sciences Research Seminars series.

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