Jinjun Huang

Global warming has exacerbated the impact of regional drought on vegetation ecosystems, especially in typical karst areas with fragile ecosystems that are more severely affected by drought. However, the response mechanisms of vegetation ecosystems in karst areas to drought stress are still uncertain. With drought stress in the summer of 2022, we examined the spatiotemporal patterns of drought in a World Heritage karst site, Guilin, China, and revealed the exacerbated drought impacts on vegetation ecosystems in karst areas with various vegetation indices. Firstly, we analyzed the spatiotemporal characteristics of drought from 2000 to 2022, utilizing the temperature vegetation dryness index (TVDI), highlighting the intra-annual variability of drought in 2022. Additionally, we compared the responses of different vegetation types to drought stress in karst and non-karst areas and explored the exacerbated impacts of drought stress on vegetation ecosystems within the same year with three vegetation indices, namely, the Normalized Difference Vegetation Index (NDVI), Leaf Area index (LAI), and Gross Primary Production (GPP) in karst areas. The results showed that drought started in July and persisted from August to November at moderate to severe levels (with severe drought in September), eventually easing in December. Karst areas exhibited severe drought (TVDI = 0.76), which more significantly impacted regional vegetation ecosystems than those in non-karst areas. Different vegetation types also experienced greater drought stress in karst areas compared to non-karst areas. The vegetation indices increased at the early- to mid-stages of drought (July to September) compared to those in the baseline year (2020–2021), mainly due to the increase in non-karst areas. However, vegetation indices decreased at the late drought stage (October to November), primarily due to the decrease in karst areas, indicating that the karst topography exacerbated the impact of drought on regional vegetation ecosystems. Since LAI and GPP exhibited similar changing patterns to TVDI, with GPP showing particularly strong alignment, they can be used to reveal the response mechanisms of ecosystems to drought stress in karst areas. We emphasize the importance of monitoring the responses of vegetation ecosystems to climate-induced droughts stress and enhancing their resilience to future climatic challenges, particularly in karst areas.

Accurately quantifying individual tree parameters is a critical step for assessing carbon sequestration in forest ecosystems. However, it is challenging to gather comprehensive tree point cloud data when using either unmanned aerial vehicle light detection and ranging (UAV-LiDAR) or terrestrial laser scanning (TLS) alone. Moreover, there is still limited research on the effect of point cloud filtering algorithms on the extraction of individual tree parameters from multiplatform LiDAR data. Here, we employed a multifiltering algorithm to increase the accuracy of individual tree parameter (tree height and diameter at breast height (DBH)) extraction with the fusion of TLS and UAV-LiDAR (TLS-UAV-LiDAR) data. The results showed that compared to a single filtering algorithm (improved progressive triangulated irregular network densification, IPTD, or a cloth simulation filter, CSF), the multifiltering algorithm (IPTD + CSF) improves the accuracy of tree height extraction with TLS, UAV-LiDAR, and TLS-UAV-LiDAR data (with R² improvements from 1% to 7%). IPTD + CSF also enhances the accuracy of DBH extraction with TLS and TLS-UAV-LiDAR. In comparison to single-platform LiDAR (TLS or UAV-LiDAR), TLS-UAV-LiDAR can compensate for the missing crown and stem information, enabling a more detailed depiction of the tree structure. The highest accuracy of individual tree parameter extraction was achieved using the multifiltering algorithm combined with TLS-UAV-LiDAR data. The multifiltering algorithm can facilitate the application of multiplatform LiDAR data and offers an accurate way to quantify individual tree parameters.