Abstract
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 ( R 2 = 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 R 2 . 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.
Highlights
The effective management of tropical forests is often dependent on high-quality information about the spatial distribution and condition of forest types [1,2,3]
The top-of-canopy height measured by Structure from Motion (SfM) was strongly correlated with the LiDAR-measured top-of-canopy height (Pearson’s r = 0.89) but the SfM measurements contained a substantial error with an root mean squared error (RMSE) of 5.08 m, 39% of the mean LiDAR-measured top-of-canopy height (Figure 3)
The linear model fit to the squared SfM measurements (Model 2) increased the R2 of the prediction by removing the nonlinearity from the relationship, which reduced the bias in the estimates for the shortest canopies, but the correction became progressively smaller in magnitude with the top-of-canopy height
Summary
The effective management of tropical forests is often dependent on high-quality information about the spatial distribution and condition of forest types [1,2,3]. Spatial measurements of forest quality are, important for prioritizing a range of conservation interventions and are especially critical in planning restoration work [4]. There are approximately 1 billion hectares of degraded tropical forests that have the potential to be restored [5], but active interventions are expensive and the cost–benefit ratios for different interventions vary with forest condition [6]. The biomass and species composition of secondary forests can vary considerably over small spatial scales, and conditions can change rapidly during natural regeneration. The planning of appropriate restoration interventions requires the timely delivery of fine-resolution forest condition data [4]
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