Articles | Volume 17, issue 6
https://doi.org/10.5194/bg-17-1673-2020
© Author(s) 2020. 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-17-1673-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
30 Mar 2020
Research article |
| 30 Mar 2020
| 30 Mar 2020
An analysis of forest biomass sampling strategies across scales
Jessica Hetzer
CORRESPONDING AUTHOR
Department of Ecological Modelling, Helmholtz Centre for Environmental
Research – UFZ, 04318 Leipzig, Germany
Andreas Huth
Department of Ecological Modelling, Helmholtz Centre for Environmental
Research – UFZ, 04318 Leipzig, Germany
Institute of Environmental Systems Research, University of Osnabrück,
49076 Osnabrück, Germany
German Centre for Integrative Biodiversity Research (iDiv),
Halle-Jena-Leipzig, 04103 Leipzig, Germany
Thorsten Wiegand
Department of Ecological Modelling, Helmholtz Centre for Environmental
Research – UFZ, 04318 Leipzig, Germany
German Centre for Integrative Biodiversity Research (iDiv),
Halle-Jena-Leipzig, 04103 Leipzig, Germany
Hans Jürgen Dobner
HTWK Leipzig – University of Applied Sciences, 04277 Leipzig, Germany
Rico Fischer
Department of Ecological Modelling, Helmholtz Centre for Environmental
Research – UFZ, 04318 Leipzig, Germany
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Cited
23 citations as recorded by crossref.
- A question of scale: modeling biomass, gain and mortality distributions of a tropical forest N. Knapp et al. https://doi.org/10.5194/bg-19-4929-2022
- Long-term changes of forest biomass and its driving factors in karst area, Guizhou, China C. Qian et al. https://doi.org/10.1177/15501477211039137
- To improve estimates of neotropical forest carbon stocks more direct measurements are needed: An example from the Southwestern Amazon A. Melo et al. https://doi.org/10.1016/j.foreco.2024.122195
- Does It Matter Whether to Use Circular or Square Plots in Forest Inventories? A Multivariate Comparison E. Velasco-Bautista et al. https://doi.org/10.3390/f15111847
- A Level-Cascaded Framework for Multiscale Forest Aboveground Biomass Estimation Using UAV-LiDAR and Public Data L. Yao et al. https://doi.org/10.1109/JSTARS.2026.3670824
- Efficiency of Data Clustering for Stratification and Sampling in the Two-Phase ALS-Enhanced Forest Stock Inventory M. Lisańczuk et al. https://doi.org/10.3390/rs17233871
- Estimation of the Total Carbon Stock of Dudles Forest Based on Satellite Imagery, Airborne Laser Scanning, and Field Surveys K. Czimber et al. https://doi.org/10.3390/f16030512
- Towards optimized sampling design for forest monitoring: A moving window analysis of scale-sensitive indices D. Wang et al. https://doi.org/10.1016/j.ecolind.2026.114990
- A novel workflow for forest regeneration prediction C. Wudel et al. https://doi.org/10.1007/s10342-026-01886-6
- Assessing scale‐dependent effects on Forest biomass productivity based on machine learning J. He et al. https://doi.org/10.1002/ece3.9110
- Are locally trained allometric functions of forest aboveground biomass universal across spatial scales and forest disturbance scenarios? B. Hartweg et al. https://doi.org/10.1016/j.ecolmodel.2025.111339
- Airborne Laser Scanning for Large-Scale Forest Carbon Quantification: A Comparison of LiDAR Single-Tree and Field-Based Methods M. Corrao et al. https://doi.org/10.3390/rs18040547
- Current and potential carbon stock in the forest communities of the Białowieża Biosphere Reserve J. Matuszkiewicz et al. https://doi.org/10.1016/j.foreco.2021.119702
- Natural forests in New Zealand – a large terrestrial carbon pool in a national state of equilibrium T. Paul et al. https://doi.org/10.1186/s40663-021-00312-0
- Blue Carbon Stock Assessment of Mangroves in Barangay Tinib, Casiguran, Aurora, through Remote Sensing and Machine Learning Regression Techniques S. Roque et al. https://doi.org/10.15684/formath.25.001
- A data-driven approach to forest health assessment through multivariate analysis and machine learning techniques R. Khan et al. https://doi.org/10.1186/s12870-025-06937-5
- Resprouting shrubs significantly contribute to Mediterranean forest carbon stocks with their root system M. Hattab et al. https://doi.org/10.1007/s10342-026-01881-x
- LiDAR-based individual tree AGB modeling of Pinus kesiya var. langbianensis by incorporating spatial structure Z. Liu et al. https://doi.org/10.1016/j.ecolind.2024.112973
- Aboveground biomass estimates of tropical mangrove forest using Sentinel-1 SAR coherence data - The superiority of deep learning over a semi-empirical model S. Ghosh & M. Behera https://doi.org/10.1016/j.cageo.2021.104737
- Mapping Amazon Forest Productivity by Fusing GEDI Lidar Waveforms with an Individual-Based Forest Model L. Bauer et al. https://doi.org/10.3390/rs13224540
- Assessing the viability of solar-biogas hybrid systems for energy provision in rural Kenyan communities S. Kimutai et al. https://doi.org/10.1016/j.seta.2025.104244
- A novel approach for estimation of aboveground biomass of a carbon-rich mangrove site in India S. Ghosh et al. https://doi.org/10.1016/j.jenvman.2021.112816
- Aboveground Biomass and Carbon Stock of Trees and Poles in Tropical Peatland Forest Ecosystems: A Case Study from Riau, Indonesia S. Prastyaningsih et al. https://doi.org/10.1051/e3sconf/202567803002
23 citations as recorded by crossref.
- A question of scale: modeling biomass, gain and mortality distributions of a tropical forest N. Knapp et al. https://doi.org/10.5194/bg-19-4929-2022
- Long-term changes of forest biomass and its driving factors in karst area, Guizhou, China C. Qian et al. https://doi.org/10.1177/15501477211039137
- To improve estimates of neotropical forest carbon stocks more direct measurements are needed: An example from the Southwestern Amazon A. Melo et al. https://doi.org/10.1016/j.foreco.2024.122195
- Does It Matter Whether to Use Circular or Square Plots in Forest Inventories? A Multivariate Comparison E. Velasco-Bautista et al. https://doi.org/10.3390/f15111847
- A Level-Cascaded Framework for Multiscale Forest Aboveground Biomass Estimation Using UAV-LiDAR and Public Data L. Yao et al. https://doi.org/10.1109/JSTARS.2026.3670824
- Efficiency of Data Clustering for Stratification and Sampling in the Two-Phase ALS-Enhanced Forest Stock Inventory M. Lisańczuk et al. https://doi.org/10.3390/rs17233871
- Estimation of the Total Carbon Stock of Dudles Forest Based on Satellite Imagery, Airborne Laser Scanning, and Field Surveys K. Czimber et al. https://doi.org/10.3390/f16030512
- Towards optimized sampling design for forest monitoring: A moving window analysis of scale-sensitive indices D. Wang et al. https://doi.org/10.1016/j.ecolind.2026.114990
- A novel workflow for forest regeneration prediction C. Wudel et al. https://doi.org/10.1007/s10342-026-01886-6
- Assessing scale‐dependent effects on Forest biomass productivity based on machine learning J. He et al. https://doi.org/10.1002/ece3.9110
- Are locally trained allometric functions of forest aboveground biomass universal across spatial scales and forest disturbance scenarios? B. Hartweg et al. https://doi.org/10.1016/j.ecolmodel.2025.111339
- Airborne Laser Scanning for Large-Scale Forest Carbon Quantification: A Comparison of LiDAR Single-Tree and Field-Based Methods M. Corrao et al. https://doi.org/10.3390/rs18040547
- Current and potential carbon stock in the forest communities of the Białowieża Biosphere Reserve J. Matuszkiewicz et al. https://doi.org/10.1016/j.foreco.2021.119702
- Natural forests in New Zealand – a large terrestrial carbon pool in a national state of equilibrium T. Paul et al. https://doi.org/10.1186/s40663-021-00312-0
- Blue Carbon Stock Assessment of Mangroves in Barangay Tinib, Casiguran, Aurora, through Remote Sensing and Machine Learning Regression Techniques S. Roque et al. https://doi.org/10.15684/formath.25.001
- A data-driven approach to forest health assessment through multivariate analysis and machine learning techniques R. Khan et al. https://doi.org/10.1186/s12870-025-06937-5
- Resprouting shrubs significantly contribute to Mediterranean forest carbon stocks with their root system M. Hattab et al. https://doi.org/10.1007/s10342-026-01881-x
- LiDAR-based individual tree AGB modeling of Pinus kesiya var. langbianensis by incorporating spatial structure Z. Liu et al. https://doi.org/10.1016/j.ecolind.2024.112973
- Aboveground biomass estimates of tropical mangrove forest using Sentinel-1 SAR coherence data - The superiority of deep learning over a semi-empirical model S. Ghosh & M. Behera https://doi.org/10.1016/j.cageo.2021.104737
- Mapping Amazon Forest Productivity by Fusing GEDI Lidar Waveforms with an Individual-Based Forest Model L. Bauer et al. https://doi.org/10.3390/rs13224540
- Assessing the viability of solar-biogas hybrid systems for energy provision in rural Kenyan communities S. Kimutai et al. https://doi.org/10.1016/j.seta.2025.104244
- A novel approach for estimation of aboveground biomass of a carbon-rich mangrove site in India S. Ghosh et al. https://doi.org/10.1016/j.jenvman.2021.112816
- Aboveground Biomass and Carbon Stock of Trees and Poles in Tropical Peatland Forest Ecosystems: A Case Study from Riau, Indonesia S. Prastyaningsih et al. https://doi.org/10.1051/e3sconf/202567803002
Saved (final revised paper)
Latest update: 07 Jun 2026
Short summary
Due to limited accessibility in tropical regions, only small parts of the forest landscape can be surveyed in forest plots. Since there is an ongoing debate about how representative estimations based on samples are at larger scales, this study analyzes how many plots are needed to quantify the biomass of the entire South American tropical forest. Through novel computational and statistical investigations we show that the spatial plot positioning is crucial for continent-wide biomass estimations.
Due to limited accessibility in tropical regions, only small parts of the forest landscape can...
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