Monitoring and analyzing the spatial and temporal changes of forest resource information

Changes of forest quality are composed by the changes in forest structure and species composition, which are usually caused by tree growth, anthropogenic activities (e.g., forest treatment, logging), and environmental disturbances (e.g., drought, flood, and wildfire). To quantitatively assess forest structure changes from multitemporal lidar data, we proposed an algorithm based on the profile area change of sequenced lidar points. The proposed method has been proven that can improve the accuracy of forest structure change detection by 80% and is more effective to detect undercanopy changes (Hu et al., 2019). On the basis of change detection results, we conducted studies on large-scale and long-term tree growth monitoring and tree growth competition studies, which provide valuable foundation for building tree growth models (Ma et al., 2017). We further investigated how variations in environmental conditions influence the spatial and temporal variations in forest structures and therefore influence the forest health. We observed significant spatial variations in crown architecture traits across large climate gradients, and crown shape related architecture traits coordinate with trunk and leaf traits tightly to balance the water and light demands and therefore adapt to changes in environmental conditions (Su et al., in press). Within the same environmental condition, large trees like Giant Sequoia are more sensitive to droughts, and the increase in temperature can further enlarge the influence of water stress on Giant Sequoia (Su et al., 2017). From the perspective of ecological modelling, we found that the neglection of the heterogeneity of forest structure in ecological models can bring significant uncertainty in the modelling results (Xue et al., 2017). To further extend the spatial scale, we evaluated the changes in the Vegetation Map of China during the last three decades, and developed an algorithm to update the Vegetation Map of China through the fusion of field data, crowdsourced data, lidar data, and satellite imagery (Su et al., in press).  These studies demonstrate how forest structure influence the distribution of light and water resources, and reveal the relationship between forest structure, forest heath, and climate change, which provide a new vantage point on predicting the ecological process under the background of global climate change.


  1. Hu Tianyu, Ma Qin, Su Yanjun, Battles John J., Collins Brandon M., Stephens Scott L., Kelly Maggi, Guo Qinghua. 2019. A simple and integrated approach for fire severity assessment using bi-temporal airborne LiDAR data. International Journal of Applied Earth Observation and Geoinformation. 78:25-38.
  2. Ma Qin, Su Yanjun, Tao Shengli, Guo Qinghua*. 2017b. Quantifying individual tree growth and tree competition using bi-temporal airborne laser scanning data: a case study in the Sierra Nevada Mountains, California. International Journal of Digital Earth. 11(5):485-503.
  3. Su Yanjun, Hu Tianyu, Wang Yongcai, Li Yumei, Dai Jingyu, Liu Hongyan, Jin Shichao, Ma Qin, Wu Jin, Liu Lingli, Fang Jingyun, Guo Qinghua*. 2020. Large-scale geographical variations and climatic controls on crown architecture traits. Journal of Geophysical Research – Biogeosciences. 125(2): e2019JG005306
  4. Su Yanjun, Bales Roger C., Ma Qin, Nydick Koren, Ray Ram L., Li Wenkai, Guo Qinghua*. 2017b. Emerging Stress and Relative Resiliency of Giant Sequoia Groves Experiencing Multiyear Dry Periods in a Warming Climate. Journal of Geophysical Research: Biogeosciences. 122(11):3063-3075.
  5. Xue BaoLin, Guo Qinghua*, Hu Tianyu, Xiao Jingfeng, Yang Yuanhe, Wang Guoqiang, Tao Shengli, Su Yanjun, Liu Jin, Zhao Xiaoqian. 2017b. Global patterns of woody residence time and its influence on model simulation of aboveground biomass. Global Biogeochemical Cycles. 31(5):821-835.
  6. Su Yanjun, Guo Qinghua*, Hu Tianyu, Guan Hongcan, Jin Shichao, An Shazhou, Chen Xuelin, Guo Ke, Hao Zhanqing, Hu Yuanman, Huang Yongmei, Jiang Mingxi, Li Zhenji, Li Xiankun, Liang Cunzhu, Liu Renlin, Liu Qing, Ni Hongwei, Peng Shaolin, Shen Zehao, Tang Zhiyao, Tian Xingjun, Wang Xihua, Wang Renqing, Xie Zongqiang, Xie Yingzhong, Xu Xiaoniu, Yang Xiaobo, Yang Yongchuan, Yu Lifei, Yue Ming, Zhang Feng, Ma Keping. 2020. An updated Vegetation Map of China (1:1000000). Science Bulletin. 65(13): 1125-1136