Langning Huo

2023 – Present     Co-supervising Luiz Henrique Cosimo, PhD student, Swedish University of Agricultural Sciences, on forest damage monitoring.

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.

2022 – 2023           Master and doctoral course: Frontier of environmental remote sensing, Beijing Forestry University

2024                       Doctoral course: Forest damage - Monitoring and environmental analysis, SLU

2024                       Doctoral course: Forest change detection with remote sensing and high-performance cloud-computing, SLU

2024                       Master course: Remote Sensing and Forest Inventory, SLU

2024 – 2026           Developing remote sensing to map forest stress using data from multiple sensors and platforms, PI, 1.6 million SEK, funded by Skogssällskapet.

2024 – 2025           Data collection for developing remote sensing of forest drought damage, PI, 1.6 million SEK, co-funded by SLU Forest Damage Center, Kempestiftelserna

2023 – 2027           Developing remote sensing techniques for monitoring forest damage and disturbance in a changing climate, SLU Forest Damage Center, PI, 2.4 million SEK 

2023 – 2025           RESDINET, Network for novel remote sensing technologies in forest disturbance ecology, Horizon Europe Framework Programme, Co-PI, coordinator of SLU, In total EUR 1 495 880, shared EUR 383,333.

2022 – 2024           Developing hyperspectral drone imagery for improved monitoring of forest insect damages, PI, 3.1 million SEK, co-funded by Kempestiftelserna, SLU Forest Damage Center, Hildur & Sven Wingquists stiftelse.

2022 – 2023           Research exchange for drone-based hyperspectral imagery for early detection of bark beetle infestations, PI, 1.6 million SEK, co-funded by Swedish innovation agency Vinnova, SLU Forest Damage Center, SLU Broderna Edlunds Scholarship. 

2022 – 2025           In conflict or collaboration? The role of forest nature conservation in the outbreak dynamics of bark beetles, Co-PI, PI, Simon Kärvemo, 3.5 million SEK funded by Formas.

2020 – 2022           Improved detection and prediction of spruce bark beetle infestations using multispectral drone images, PI, 2.6 million SEK, co-funded by Stiftenlsen Seydlitz MP bolagen, Stiftelsen Nils och Dorthi Troëdssons forskningsfond, Hildur & Sven Wingquists stiftelse, SLU Forest Damage Center.

2020 – 2024           Mapping forest parameters and forest damage for sustainable forest management from data fusion of satellite data, MOST/ESA Dragon 5 Cooperation, PI.

1.      Huo, L., Persson, H.J., & Lindberg, E. (2024). Analyzing the environmental risk factors of European spruce bark beetle damage at the local scale. European Journal of Forest Research, 137, 675.

2.      Li, N., Huo, L., & Zhang, X. (2024). Using only the red-edge bands is sufficient to detect tree stress: A case study on the early detection of PWD using hyperspectral drone images. Computers and Electronics in Agriculture, 217, 108665.

3.      Kärvemo, S.; Huo, L.; Öhrn, P.; Lindberg, E.; Persson, H. J. (2023): Different triggers, different stories: Bark-beetle infestation patterns after storm and drought-induced outbreaks. In Forest Ecology and Management 545 (1), p. 121255. DOI: 10.1016/j.foreco.2023.121255.

4.      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.

5.      Huo, L., Persson, H.J., Bohlin, J., & Lindberg, E. (2023). Green Attack or Overfitting? Comparing Machine-Learning- and Vegetation-Index-Based Methods to Early Detect European Spruce Bark Beetle Attacks Using Multispectral Drone Images. In IGARSS 2023 IEEE International Geoscience and Remote Sensing Symposium: IEEE.

6.      Li, N., Huo, L., & Zhang, X. (2023). Exploring Common Hyperspectral Features of Early-Stage Pine Wilt Disease at Different Scales, for Different Pine Species, and at Different Regions. In IGARSS 2023 IEEE International Geoscience and Remote Sensing Symposium: IEEE.

7.      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.

8.      Li, N., Huo, L., Zhang, X., (2022). Classification of pine wilt disease at different infection stages by diagnostic hyperspectral bands. Ecological Indicators, 142, 109198. 

9.      Huo, L., Lindberg, E., Fransson, J.E.S., & Persson, H.J. (2022). Comparing Spectral Differences Between Healthy and Early Infested Spruce Forests Caused by Bark Beetle Attacks using Satellite Images. In IGARSS 2022 IEEE International Geoscience and Remote Sensing Symposium (pp. 7709–7712): IEEE.

10.   Li, N., Zhang, X., & Huo, L. (2022). Identifying Nematode-Induced Wilt Using Hyperspectral Drone Images and Assessing the Potential of Early Detection. In IGARSS 2022 IEEE International Geoscience and Remote Sensing Symposium (pp. 512–515): IEEE.

11.   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. (1% top-cited research papers in the academic field of Geosciences in 2023)

12.   Huo, L., Lindberg, E., & Persson, H. (2020). Normalized Projected Red & SWIR (NPRS): A New Vegetation Index for Forest Health Estimation and Its Application on Spruce Bark Beetle Attack Detection. In IGARSS 2020 IEEE International Geoscience and Remote Sensing Symposium (pp. 4618–4621): IEEE.

13.   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. 

14.   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.