Abstract. Lianas, woody vines, are a key component of tropical forest because they may reduce carbon storage potential. Lianas are increasing in density and biomass in tropical forests, but it is unknown what the potential consequences of these increases are for forest dynamics. Lianas may proliferate in disturbed areas, such as regenerating forests, but little is known about the role of lianas in secondary succession. In this study, we evaluated the potential of the ground LiDAR to detect differences in the vertical structure of stands of different ages with and without lianas in tropical dry forests. Specifically, we used a terrestrial laser scanner called VEGNET to assess whether liana presence influences the vertical signature of stands of different ages, and whether successional trajectories as detected by the VEGNET could be altered by liana presence. We deployed the VEGNET ground LiDAR system in 15 secondary forests of different ages early (21 years old since land abandonment), intermediate (32–35 years old) and late stages (> 80 years old) with and without lianas. We compared laser-derived vegetation components such as Plant Area Index (PAI), plant area volume density (PAVD), and the radius of gyration (RG) across forest stands between liana and no-liana treatments. In general forest stands without lianas show a clearer distinction of vertical strata and the vertical height of accumulated PAVD. A significant increase of PAI was found from intermediate to late stages in stands without lianas, but in stands where lianas were present there was not a significant trend. This suggests that lianas may be influencing successional trajectories in secondary forests, and these effects can be captured by terrestrial laser scanners such as the VEGNET. This research contributes to estimate the potential effects of lianas in secondary dry forests and highlight the role of ground LiDAR to monitor structural changes in tropical forests due to liana presence.
SOCIO-CULTURAL FOUNDATIONS OF THE CHINESE MODEL OF FUTURE MANAGEMENT