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Langning Huo

Langning Huo


2016 – 2017  Forestry remote sensing and geographic information system (Practice), Beijing Forestry University


2021 – 2022  Improved detection and prediction of spruce bark beetle infestations, Stiftenlsen Seydlitz MP bolagen, PI.

2020 – 2022  Satellites and drones in the future forest remote sensing research, Hildur och Sven Wingquists foundation for forest research, Co-PI.

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.

2020 – 2021  How does forest conservation influence the risk of bark beetle damages? Skogssällskapet.

2020 – 2021  Estimating Forest Resources and Quality-related Attributes Using Automated Methods and Technologies, ForestQuality.

2019 – 2023  Mistra Digital Forest, Mistra.

2019 – 2020  Detektion av granbarkborreangrepp från nya fjärranalysdata och kontinuitet i data från Remningstorp, Hildur och Sven Wingquists foundation for forest research.

2017 – 2018  Ny teknik för skogsbruksplanering (New techniques for forest management planning), Hildur och Sven Wingquists foundation for forest research.

2017 – 2020  Single-tree level precise monitoring of plantation by remote sensing technology, China Innovation Funding.

2016 – 2017  Key techniques of monitoring and early warning for major forest pest plagues, National Forestry and Grassland Administration of China.


2020 – 2024  MOST/ESA Dragon 5 Cooperation, with Beijing Forestry University (China), Mapping forest parameters and forest damage for sustainable forest management from data fusion of satellite data, Co-PI.

Selected publications

Huo L, Persson H, Lindberg E., 2020. 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(24):9525-44.

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.

Huo, L., Zhang, X., 2019. Extracting Single-tree Information from LiDAR data by a hierarchical clustering method. Scientia Silvae Sinicae; Accepted.

Huo, L., Zhang, X., 2019. A benchmark applying bidirectional judgement principle to match trees extracting from remote sensing data with field-measured trees. Scientia Silvae Sinicae; Accepted.

Zhang, N., Zhang, X., Yang, G., Zhu, C., Huo, L., Feng, H., 2018. Assessment of defoliation during the Dendrolimus tabulaeformis Tsai et Liu disaster outbreak using UAV-based hyperspectral images. Remote Sensing of Environment 217, 323–339.


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