Consistent patterns of LiDAR-derived measures of savanna vegetation complexity between wet and dry seasons - Zhengyang Wang; Jenia Singh; Andrew B. Davies (Ecological Indicators)
Metrics of structural vegetation complexity derived from airborne Light Detection and Ranging (LiDAR) have been used extensively in studies on plant ecology, animal behavior and the distribution of biodiversity. However, the acquisition of remotely-sensed data from the air and the surveying of biological information on the ground are often temporally disjunct and collected in different seasons. It is assumed that seasonal variation in vegetation structure is not substantive enough to affect landscape-level correlational analyses, but this assumption has never been explicitly tested in savanna ecosystems. Moreover, phenological differences in vegetation are thought to adversely affect the comparability of LiDAR measurements from different seasons, but these effects have seldom been tested and could depend on data resolution. We used repeat ultrahigh-resolution LiDAR data (>100 points/m2) acquired in the wet and dry season in Kruger National Park, South Africa, to evaluate the consistency of the spatial distribution of vegetation structural complexity across the landscape. We computed 16 commonly used metrics of vegetation complexity (derived from LiDAR canopy height models and point clouds), and found that patches of the landscape that were vegetatively complex in the wet season LiDAR data remained so in the dry season LiDAR dataset (although phenological differences were detected within individual patches). We repeated our analysis at a thinned point cloud density of 10 points/m2 and found that seasonal consistencies were not resolution dependent. Our findings provide validation for correlational studies that employ seasonally disjunct LiDAR and field data.