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Langning Huo

Langning Huo
Researcher in forest remote sensing, specializing in developing remote sensing solutions for monitoring forest structure and disturbance, from individual-tree scale to landscape scale. Project leader of ongoing projects on remote sensing for bark beetle damages, including early detection, damage mapping, environmental modeling, and risk estimation. Developing remote sensing techniques include drone-based multispectral and hyperspectral imagery, high-resolution satellite imagery, and airborne and terrestrial laser scanning.


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.

Selected publications

·         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.


Researcher at the Department of Forest Resource Management; Division of Forest Remote Sensing
Telephone: +46907868524
Postal address:
Institutionen för skoglig resurshushållning
Avdelningen för skoglig fjärranalys
901 83 Umeå
Visiting address: Skogsmarksgränd, Umeå