Articles | Volume 15, issue 2
https://doi.org/10.5194/bg-15-399-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/bg-15-399-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
18 Jan 2018
Research article |
| 18 Jan 2018
| 18 Jan 2018
An enhanced forest classification scheme for modeling vegetation–climate interactions based on national forest inventory data
Norwegian Institute of Bioeconomy Research (NIBIO), Department of Forest and Climate, 1431 Ås, Norway
Stephanie Eisner
Norwegian Institute of Bioeconomy Research (NIBIO), Department of Forest and Climate, 1431 Ås, Norway
Rasmus Astrup
Norwegian Institute of Bioeconomy Research (NIBIO), Department of Forest and Climate, 1431 Ås, Norway
Jonas Fridman
Swedish University of Agricultural Sciences (SLU), 901 83 Umeå, Sweden
Ryan M. Bright
Norwegian Institute of Bioeconomy Research (NIBIO), Department of Forest and Climate, 1431 Ås, Norway
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Cited
17 citations as recorded by crossref.
- Regional temperature response to different forest development stages in Fennoscandia explored with a regional climate model B. Huang et al. 10.1016/j.agrformet.2024.110083
- Estimation of the Canopy Height Model From Multispectral Satellite Imagery With Convolutional Neural Networks S. Illarionova et al. 10.1109/ACCESS.2022.3161568
- Improving the representation of high-latitude vegetation distribution in dynamic global vegetation models P. Horvath et al. 10.5194/bg-18-95-2021
- Quantifying Regional Surface Energy Responses to Forest Structural Change in Nordic Fennoscandia Y. Kumkar et al. 10.1029/2019JD032092
- Dominant Tree Species Mapping Using Machine Learning Based on Multi-Temporal and Multi-Source Data H. Guo et al. 10.3390/rs16244674
- Representation of tree cover in global land cover products: Finland as a case study area T. Majasalmi & M. Rautiainen 10.1007/s10661-021-08898-2
- A simple grid-based framework for simulating forest structural trajectories linked to transient forest management scenarios in Fennoscandia T. Majasalmi et al. 10.1007/s10584-020-02742-1
- Global patterns and changes of carbon emissions from land use during 1992–2015 S. Tian et al. 10.1016/j.ese.2021.100108
- A simplified multi-model statistical approach for predicting the effects of forest management on land surface temperature in Fennoscandia B. Huang et al. 10.1016/j.agrformet.2023.109362
- Compositional and structural stratification does not improve direct estimation of Sentinel-2-derived surface albedo in Fennoscandian forests R. Bright & E. Ramtvedt 10.1016/j.agrformet.2024.110251
- Evaluation of two new-generation global soil databases for macro-scale hydrological modelling in Norway S. Huang et al. 10.1016/j.jhydrol.2022.127895
- The potential impacts of climate and forest changes on streamflow for micro-, meso- and macro-scale catchments in Norway S. Huang et al. 10.1016/j.ejrh.2024.102147
- Inferring Surface Albedo Prediction Error Linked to Forest Structure at High Latitudes R. Bright et al. 10.1029/2018JD028293
- Improvements of the spatially distributed hydrological modelling using the HBV model at 1 km resolution for Norway S. Huang et al. 10.1016/j.jhydrol.2019.03.051
- Constraining the HBV model for robust water balance assessments in a cold climate H. Erlandsen et al. 10.2166/nh.2021.132
- A Survey of Computer Vision Techniques for Forest Characterization and Carbon Monitoring Tasks S. Illarionova et al. 10.3390/rs14225861
- Influence of Spatial Resolution on Snow Cover Dynamics for a Coastal and Mountainous Region at High Latitudes (Norway) J. Magnusson et al. 10.1029/2019WR024925
16 citations as recorded by crossref.
- Regional temperature response to different forest development stages in Fennoscandia explored with a regional climate model B. Huang et al. 10.1016/j.agrformet.2024.110083
- Estimation of the Canopy Height Model From Multispectral Satellite Imagery With Convolutional Neural Networks S. Illarionova et al. 10.1109/ACCESS.2022.3161568
- Improving the representation of high-latitude vegetation distribution in dynamic global vegetation models P. Horvath et al. 10.5194/bg-18-95-2021
- Quantifying Regional Surface Energy Responses to Forest Structural Change in Nordic Fennoscandia Y. Kumkar et al. 10.1029/2019JD032092
- Dominant Tree Species Mapping Using Machine Learning Based on Multi-Temporal and Multi-Source Data H. Guo et al. 10.3390/rs16244674
- Representation of tree cover in global land cover products: Finland as a case study area T. Majasalmi & M. Rautiainen 10.1007/s10661-021-08898-2
- A simple grid-based framework for simulating forest structural trajectories linked to transient forest management scenarios in Fennoscandia T. Majasalmi et al. 10.1007/s10584-020-02742-1
- Global patterns and changes of carbon emissions from land use during 1992–2015 S. Tian et al. 10.1016/j.ese.2021.100108
- A simplified multi-model statistical approach for predicting the effects of forest management on land surface temperature in Fennoscandia B. Huang et al. 10.1016/j.agrformet.2023.109362
- Compositional and structural stratification does not improve direct estimation of Sentinel-2-derived surface albedo in Fennoscandian forests R. Bright & E. Ramtvedt 10.1016/j.agrformet.2024.110251
- Evaluation of two new-generation global soil databases for macro-scale hydrological modelling in Norway S. Huang et al. 10.1016/j.jhydrol.2022.127895
- The potential impacts of climate and forest changes on streamflow for micro-, meso- and macro-scale catchments in Norway S. Huang et al. 10.1016/j.ejrh.2024.102147
- Inferring Surface Albedo Prediction Error Linked to Forest Structure at High Latitudes R. Bright et al. 10.1029/2018JD028293
- Improvements of the spatially distributed hydrological modelling using the HBV model at 1 km resolution for Norway S. Huang et al. 10.1016/j.jhydrol.2019.03.051
- Constraining the HBV model for robust water balance assessments in a cold climate H. Erlandsen et al. 10.2166/nh.2021.132
- A Survey of Computer Vision Techniques for Forest Characterization and Carbon Monitoring Tasks S. Illarionova et al. 10.3390/rs14225861
Latest update: 30 Mar 2025
Short summary
Forest management shapes forest structure and in turn surface–atmosphere interactions. We used Fennoscandian forest maps and inventory data to develop a classification system for forest structure. The classification was integrated with the ESA Climate Change Initiative land cover map to achieve complete surface representation. The result is an improved product for modeling surface–atmosphere exchanges in regions with intensively managed forests.
Forest management shapes forest structure and in turn surface–atmosphere interactions. We used...
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