Abstract
The diameter distribution of savanna tree populations is a valuable indicator of savanna health because changes in the number and size of trees can signal a shift from savanna to grassland or forest. Savanna diameter distributions have traditionally been monitored with forestry techniques, where stem diameter at breast height (DBH) is measured in the field within defined sub-hectare plots. However, because the spatial scale of these plots is often misaligned with the scale of variability in tree populations, there is a need for techniques that can scale-up diameter distribution surveys. Dense point clouds collected from uncrewed aerial vehicle laser scanners (UAV-LS), also known as drone-based LiDAR (Light Detection and Ranging), can be segmented into individual tree crowns then related to stem diameter with the application of allometric scaling equations. Here, we sought to test the potential of UAV-LS tree segmentation and allometric scaling to model the diameter distributions of savanna trees. We collected both UAV-LS and field-survey data from five one-hectare savanna woodland plots in northern Australia, which were divided into two calibration and three validation plots. Within the two calibration plots, allometric scaling equations were developed by linking field-surveyed DBH to the tree metrics of manually delineated tree crowns, where the best performing model had a bias of 1.8% and the relatively high RMSE of 39.2%. A segmentation algorithm was then applied to segment individual tree crowns from UAV-LS derived point clouds, and individual tree level segmentation accuracy was assessed against the manually delineated crowns. 47% of crowns were accurately segmented within the calibration plots and 68% within the validation plots. Using the site-specific allometry, DBH was modelled from crown metrics within all five plots, and these modelled results were compared to field-surveyed diameter distributions. In all plots, there were significant differences between field-surveyed and UAV-LS modelled diameter distributions, which became similar at two of the plots when smaller trees (<10 cm DBH) were excluded. Although the modelled diameter distributions followed the overall trend of field surveys, the non-significant result demonstrates a need for the adoption of remotely detectable proxies of tree size which could replace DBH, as well as more accurate tree detection and segmentation methods for savanna ecosystems.
Highlights
The performance of the models relating field-surveyed diameter at breast height (DBH) to the crown diameter, tree height, and the compound variable of height x crown diameter (HCD) is shown in Figure 4, with derived regression parameters for the parametric models shown in equations Appendix B (Equations (A1)–(A7))
This study set out to assess whether uncrewed aerial vehicle laser scanners (UAV-LS) point clouds could be used to model the diameter distribution of savanna trees at the plot level
We reported relatively high error for all models linking DBH to crown size and height metrics, which was attributed to disturbance driven variability in savanna tree allometry
Summary
Savannas are a globally important biome that cover around one-fifth of the earths land surface [1,2]. Defined by a discontinuous tree layer with a grassy understorey, savanna structure sits on the continuum between forests and grasslands [3,4], and changes to disturbance regimes and biophysical conditions can alter tree populations to the extent that they switch to a different vegetation type entirely [5,6]. Long-term savanna fire exclusion has led to increases in stem density, often causing vegetation to shift to forests [7]. Persistent high-intensity fires can limit the growth of trees to larger size-classes, reducing canopy cover and shifting vegetation toward more open grasslands [8].
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