2022 Master and doctoral course: Frontier in Remote Sensing, Beijing Forestry University
2016 – 2017 Bachelor course: Forestry remote sensing and geographic information system, Beijing Forestry University
2023 – 2025 RESDINET, Network for novel remote sensing technologies in forest disturbance ecology, Horizon Europe Framework Programme, Co-PI.
2022 – 2023 Developing drone-based hyperspectral imagery for forest infestation detection, Vinnova, PI.
2022 – 2023 “Flying tree healthy analyzer”: drone-based hyperspectral imagery for early detection of bark beetle infestations. SLU Forest Damage Center, PI.
2022 – 2023 Research exchange for drone-based hyperspectral imagery for early detection of bark beetle infestations. SLU Forest Damage Center, PI.
2022 – 2023 A prolonged observation series of bark beetle infestations from the field and remote sensing, Hildur & Sven Wingquists stiftelse för skogsvetenskaplig forskning, PI.
2022 – 2025 In conflict or collaboration? The role of forest nature conservation in the outbreak dynamics of bark beetles, Co-PI, PI, Simon Kärvemo.
2021 – 2022 Developing forecasting methods of spruce bark beetle infestation by exploring environmental driven factors, Stiftelsen Nils och Dorthi Troëdssons forskningsfond, PI.
2021 – 2022 Detection of spruce bark beetle infestations from remote sensing data, SLU Forest Damage Center, PI.
2021 – 2022 Improved detection and prediction of spruce bark beetle infestations, Stiftenlsen Seydlitz MP bolagen, PI.
2021 – 2022 Satellites and drones in the future forest remote sensing research, Hildur och Sven Wingquists foundation for forest research, Co-PI, PI: Henrik Persson.
2020 – 2024 Mapping forest parameters and forest damage for sustainable forest management from data fusion of satellite data, MOST/ESA Dragon 5 Cooperation, Co-PI, PI: Johan Fransson.
2020 – 2021 How does forest conservation influence the risk of bark beetle damages? Skogssällskapet, PI: Simon Kärvemo.
2022 – Present Co-supervising Cameron Pellett, PhD student, Swedish University of Agricultural Sciences.
2022 – Present Co-supervising Run Yu, PhD student, Beijing Forestry University, on forest damage detection.
2021 – Present Co-supervising Niwen Li, PhD student, Beijing Forestry University, on forest damage detection.
2021 – Present Co-supervising Yining Lian, a PhD student in Beijing Forestry University, on laser drone data for forestry.
· Huo, L., Lindberg, E., Bohlin, J., & Persson, H.J. (2023). Assessing the detectability of European spruce bark beetle green attack in multispectral drone images with high spatial- and temporal resolutions. Remote Sensing of Environment, 287, 113484.
· Huo, L., Strengbom, J., Lundmark, T., Westerfelt, P., & Lindberg, E. (2023). Estimating the conservation value of boreal forests using airborne laser scanning. Ecological Indicators, 147, 109946
· Yu, R., Huo, L., Huang, H., Yuan, Y., Gao, B., Liu, Y., Yu, L., Li, H., Yang, L., Ren, L., & Luo, Y. (2022). Early detection of pine wilt disease tree candidates using time-series of spectral signatures. Frontiers in Plant Science, 13, 48.
· Li, N., Huo, L., Zhang, X., (2022). Classification of pine wilt disease at different infection stages by diagnostic hyperspectral bands. Ecological Indicators, 142, 109198.
· Huo, L., Lindberg, E., & Holmgren, J. (2022). Towards low vegetation identification: A new method for tree crown segmentation from LiDAR data based on a symmetrical structure detection algorithm (SSD). Remote Sensing of Environment, 270, 112857.
· Huo, L., Persson, H., Lindberg, E., (2021). Early detection of forest stress from European spruce bark beetle attack, and a new vegetation index: Normalized Distance Red & SWIR (NDRS). Remote Sensing of Environment, 255, 112240.
· Huo, L., & Lindberg, E. (2020). Individual tree detection using template matching of multiple rasters derived from multispectral airborne laser scanning data. International Journal of Remote Sensing, 41, 9525–9544.
· Huo, L., & Zhang, X. (2019). A new method of equiangular sectorial voxelization of single-scan terrestrial laser scanning data and its applications in forest defoliation estimation. ISPRS Journal of Photogrammetry and Remote Sensing, 151, 302–312.
· Huo, L., Zhang, N., Zhang, X., & Wu, Y. (2019). Tree defoliation classification based on point distribution features derived from single-scan terrestrial laser scanning data. Ecological Indicators, 103, 782–790.