Measuring forests with UAVs

Accurate measurement of tropical forest canopy heights and aboveground carbon using structure from motion

Remote Sensing, 11(8), 928, 1-16 (2019)

Tom Swinfield1,2,3, Jeremy A. Lindsell4, Jonathan V. Williams2, Rhett D. Harrison5, Agustiono3, Habibi3, Elva Gemita3, Carola B. Schönlieb6 & David A. Coomes2

1 Centre for Conservation Science, Royal Society for Protection of Birds, David Attenborough Building, Pembroke Street, Cambridge CB2 3QY, UK
2 Forest Ecology and Conservation Group, Department of Plant Sciences, Downing Street, Cambridge CB2 3EA, UK
3 PT Restorasi Ekosistem Indonesia, Jl. Dadali No. 32, Bogor 16161, Indonesia
4 A Rocha International, David Attenborough Building, Pembroke Street, Cambridge CB2 3QY, UK
5 World Agroforestry Centre, Eastern Southern African Region, 13 Elm Road, Woodlands, Lusaka 999134, Zambia
6 Department of Applied Mathematics and Theoretical Physics (DAMTP), University of Cambridge, Wilberforce Road, Cambridge CB3 0WA, UK


Unmanned aerial vehicles are increasingly used to monitor forests. Three-dimensional models of tropical rainforest canopies can be constructed from overlapping photos using Structure from Motion (SfM), but it is often impossible to map the ground elevation directly from such data because canopy gaps are rare in rainforests. Without knowledge of the terrain elevation, it is, thus, difficult to accurately measure the canopy height or forest properties, including the recovery stage and aboveground carbon density. Working in an Indonesian ecosystem restoration landscape, we assessed how well SfM derived the estimates of the canopy height and aboveground carbon density compared with those from an airborne laser scanning (also known as LiDAR) benchmark. SfM systematically underestimated the canopy height with a mean bias of approximately 5 m. The linear models suggested that the bias increased quadratically with the top-of-canopy height for short, even-aged, stands but linearly for tall, structurally complex canopies (>10 m). The predictions based on the simple linear model were closely correlated to the field-measured heights when the approach was applied to an independent survey in a different location ( R2 = 67% and RMSE = 1.85 m), but a negative bias of 0.89 m remained, suggesting the need to refine the model parameters with additional training data. Models that included the metrics of canopy complexity were less biased but with a reduced R2 . The inclusion of ground control points (GCPs) was found to be important in accurately registering SfM measurements in space, which is essential if the survey requirement is to produce small-scale restoration interventions or to track changes through time. However, at the scale of several hectares, the top-of-canopy height and above-ground carbon density estimates from SfM and LiDAR were very similar even without GCPs. The ability to produce accurate top-of-canopy height and carbon stock measurements from SfM is game changing for forest managers and restoration practitioners, providing the means to make rapid, low-cost surveys over hundreds of hectares without the need for LiDAR.

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