Development of near-surface remote sensing platforms for forest resource information acquisition

Light detection and ranging (lidar), an active remote sensing technology, has the abilities of high-precision and strong penetration to vegetation, which shows unparalleled advantages in obtaining forest resource information comparing with other traditional remote sensing technologies. In this context, we designed and developed a series of near-surface remote sensing systems, which provide a new solution for accurately and effectively acquire forest resource information at forest stand and landscape scales. These systems are equipped with various remote sensing sensors (e.g., camera, multispectral imager, hyperspectral imager, thermal imager, and laser scanner), and can be mounted on various near-surface platforms (e.g., drones, backpack, mobile vehicles). These systems have the advantages of high stability, high accuracy, high efficiency, and high flexibility, which solves the problem of acquiring near-surface remote sensing data with high precision under GPS-denial environments (e.g., dense forest) and low platform stability (Guo et al., 2016; Guo et al., 2017; Guo et al., 2017). Comparing to traditional forest inventory methods, the acquisition efficiency of forest resource information using the developed near-surface remote sensing platforms has been increased by more than ten times. We further developed an algorithm that can fuse multiplatform lidar data based on individual tree locations, which provide an automatic solution for fusing multiplatform lidar data for acquiring more complete forest structural parameters (Guan et al., 2020). These developed systems can be used to carry out the task of monitoring three-dimensional (3D) structure dynamics of forest vegetation from individual to national and global scales, which can promote the vegetation and biodiversity studies as well as provide accurate and reliable decision-making information to forest managers.


  1. Guo Qinghua*, Liu Jin, Li Yumei, Zhai Qiuping, Wang Yongcai, Wu Fangfang, Hu Tianyu, Wan Huawei, Liu Huiming, Shen Wenming. 2016. A near-surface remote sensing platform for biodiversity monitoring: perspectives and prospects. Biodiversity Science. 24(11): 1249-1266. (in Chinese).
  2. Guo Qinghua*, Su Yanjun, Hu Tianyu, Zhao Xiaoqian, Wu Fangfang, Li Yumei, Liu Jin, Chen Linhai, Xu Guangcai, Lin Guanghui, Zheng Yi, Lin Yiqiong, Mi Xiangcheng, Fei Lin, Wang Xugao. 2017a. An integrated UAV-borne lidar system for 3D habitat mapping in three forest ecosystems across China. International Journal of Remote Sensing.38(8-10):2954-2972.
  3. Guo Qinghua*, Hu Tianyu, Jiang Yuanxi, Jin Shichao, Wang Rui, Guan Hongcan, Yang Qiuli, Li Yumei, Wu Fangfang, Zhai Qiuping, Liu Jin, Su Yanjun. 2018. Advances in remote sensing application for biodiversity research. Biodiversity Science. 26(8):789-806.(In Chinese).
  4. Zhang Jing, Sun Qianhui, Ye Zhen, Yang Mohan, Zhao Xiaoxia, Ju Yuanzhen, Hu Tianyu, Guo Qinghua*. New Technology for Ecological Remote Sensing: Light, Small Unmanned Aerial Vehicles (UAV). Tropical Geography. 39(4): 604-615. (In chinese)
  5. Guan Hongcan, Su Yanjun , Hu Tianyu, Wang Rui, Ma Qin, Yang Qiuli, Sun Xiliang, Li Yumei, Jin Shichao, Zhang Jing, Ma Qin, Guo Qinghua. 2019. A Novel Framework to Automatically Fuse Multi-platform Lidar Data in Forest Environments Based on Tree Locations. IEEE Transactions on Geoscience and Remote Sensing. (Accepted)