Demonstrating the potential of automated laser scanning to capture forest structural dynamics

Recent advancements in low-cost automated lidar sensors enable high-frequency data collection with minimal human intervention while retaining the benefits of active lidar methods. These technologies are particularly valuable for capturing seasonal changes in forest ecosystems, improving our understanding of phenology, growth cycles, and ecosystem responses to environmental shifts. Firstly, we compare the ability of the sensor to characterise forest canopy to that of industry-standard TLS systems. Secondly, we demonstrate the monitoring of seasonal changes in phenology via daily estimates of gap-fraction-based plant area index (PAI) and plant area volume density (PAVD) profiles. The LEAF sensor (905 nm) acquires data in either hemispherical (360° in azimuth and 0°-130° in zenith) or hinge (five rings centred around 57.5° in zenith) modes, at a maximum resolution of 0.45°. We used the hinge gap-fraction estimates to derive the vertical PAVD profiles, which are, in theory, not influenced by the leaf angle distribution. We also acquired TLS scans using the RIEGL VZ400i at an angular resolution of 0.03° for comparisons. Furthermore, to capture seasonal changes, daily automated scans were done in temperate lowland and mountain forests in Belgium and Germany, respectively. The vegetation profiles (PAVD) derived from the LEAF sensors differ from those from the VZ400i regarding absolute values but show similar trends. The profiles from both the sensors were rescaled, and a Spearman correlation analysis comparing two PAVD profiles showed a high correlation (ρ = 0.89, p < 0.01, n = 40), demonstrating the potential of automated scanners to characterise canopy structure accurately. Preliminary systematic comparisons with the RIEGL VZ400i suggest their potential reliability. Using daily acquisitions of different temperate forest types, we highlight both the potential and limitations of these sensors in capturing seasonal canopy changes.