Vegetation variables estimation and mapping using close-range sensing techniques

To better monitor canopy structure and function at very fine scale, we develop algorithms to estimate fractional cover (fCover), leaf area index (LAI), fractional intercepted photosynthesis active radiation (fIPAR), and advanced biochemical traits such as water content and leaf chlorophyll content (LCC).

  • Li, L., Mu, X.*, Macfarlane, C., Song, W., Chen, J., Yan, K., Yan, G., 2018. A half-Gaussian fitting method for estimating fractional vegetation cover of corn crops using unmanned aerial vehicle images. Agric. For. Meteorol. 262, 379–390. [HTML]
  • Yan, G., Li, L.*, Coy, A., Mu, X., Chen, S., Xie, D., Zhang, W., Shen, Q., Zhou, H., 2019. Improving the estimation of fractional vegetation cover from UAV RGB imagery by colour unmixing. ISPRS J. Photogramm. Remote Sens. 158, 23–34. [HTML]
  • Li, L., Chen, J., Mu*, X., Li, W., Yan, G., Xie, D., Zhang, W., 2020. Quantifying understory and overstory vegetation cover using UAV-based RGB imagery in forest plantation. Remote Sens. 2020, Vol. 12, Page 298 12, 298. [HTML]
  • S Cai, W Zhang, S Jin, J Shao, L Li, S Yu, G Yan, 2021. Improving the estimation of canopy cover from UAV-LiDAR data using a pit-free CHM-based method. International Journal of Digital Earth. Page 1-16. [HTML]
  • Qi, J., Xie, D.*, Li, L., Zhang, W., Mu, X., Yan, G., 2019. Estimating Leaf Angle Distribution From Smartphone Photographs. IEEE Geosci. Remote Sens. Lett. 16, 1190–1194. [HTML]

Reflectance anisotropy characterization and modeling (BRDF)

As UAVs can operate multiangular observation with flexible pattern and measure surface anisotropy with improved angular sampling and high ground sample distance relative to satellite sensors, we deeply explore the reflectance anisotropy of canopy and background (Dr. Linyuan Li). Based on the field observations, we advance the modeling of the radiative transfer process of light at multiple scales and release a 3D RTM called LESS (developer: Dr. Jianbo Qi).

  • Li, L., Mu, X.*, Qi, J., Pisek, J., Roosjen, P., Yan, G., Huang, H., Liu, S., Baret, F., 2021. Characterizing soil background BRDF in open forests using UAV-based multi-angular images. ISPRS J. Photogramm. Remote Sens. [HTML]
  • Qi, J., Xie, D.*, Yin, T., Yan, G., Gastellu-Etchegorry, J.-P., Li, L., Zhang, W., Mu, X., Norford, L.K., 2019. LESS: LargE-Scale remote sensing data and image simulation framework over heterogeneous 3D scenes. Remote Sens. Environ. 221, 695–706. [HTML]

LiDAR measurements, processing, and fusion with optical images

LiDAR has unprecedented ability to sample vegetation canopies. However, the scan design of terrestrial LiDAR in forests and the processing of 3D point clouds into interested structural parameters are not fully established. As the fusion data of image and point cloud generally can improve the ability of canopy variable retrieval, the fusing algorithm needs to be developed.

  • Li, L., Mu, X.*, Soma, M., Wan, P., Qi, J., Hu, R., Tong, Y., Zhang, W., Yan, G., 2020. An iterative-mode scan design of terrestrial laser scanning in forests for minimising occlusion effect. IEEE Trans. Geosci. Remote Sens. [HTML]
  • Zhang, W.*, Zhao, J., Chen, M., Chen, Y., Yan, K., Li, L., Qi, J., Wang, X., Luo, J., Chu, Q., 2015. Registration of optical imagery and LiDAR data using an inherent geometrical constraint. Opt. Express 23, 7694. [HTML]