Our study reveals that canopy structure significantly influences local-scale variations in autumn phenology by mediating microclimate conditions. Incorporating the “canopy structure-microclimate-autumn phenology” pathway into existing models has improved prediction accuracy and enhanced forecasts of future changes in autumn phenology. These findings provide new insights into the mechanisms driving local-scale variation in temperate forest autumn phenology and its response to climate change. (learn more)

Achieving accurate high-resolution estimates of grassland canopy cover at a large spatial scale remains challenging due to the limited spatial coverage of field measurements and the scale mismatch between field measurements and satellite imagery. We addressed these challenges by proposing a regression-based approach to estimate large-scale grassland canopy cover, leveraging the integration of drone imagery and multisource remote sensing data (learn more). The generated dataset has been made available online through the Plant Science Data Center of Chinese Academy of Sciences (https://www.plantplus.cn/doi/10.12282/plantdata.1607).

The lab members jointly celebrate Teacher's Day and we extend our heartfelt wishes to all educators.

In this work, we developed a novel L1-Tree algorithm for constructing 3D tree models from TLS data, which achieved a high branch identification accuracy and branch architectural trait estimation accuracy across branch orders. The code for the proposed L1-Tree algorithm has been made available online through GitHub (https://github. com/LidarSu/L1-Tree).

Congratulations to Xiaoqiang Liu for receiving the Ecological Society of America's (ESA) Asian Ecology Section Outstanding Student Award！

We are pleased to announce that three graduates have successfully completed their studies. To celebrate and commemorate this achievement, we organized a series of events. We will cherish the time we spent working and living together and sincerely wish them great success in their new endeavors.

We went to Beijing Dongling Mountain with members of Lingli Liu's research group for fieldwork, including setting up experimental plots and collecting LiDAR data from the experimental forest plots. In our free time, we enjoyed some recreational activities together including prepared and shared dinner.

We mapped the global distribution of forest canopy structural complexity (CSC) and revealed the factors influencing its distribution using worldwide light detection and ranging data. We find that forest CSC predominantly demonstrates significant positive relationships with forest ecosystem productivity and stability globally, although substantial variations exist among forest ecoregions. (learn more)