Articles | Volume 22, issue 16
https://doi.org/10.5194/bg-22-4291-2025
© Author(s) 2025. 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-22-4291-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
28 Aug 2025
Research article |
| 28 Aug 2025
| 28 Aug 2025
Assessing the effect of forest management on above-ground carbon stock by remote sensing
Sofie Van Winckel
Department of Earth and Environmental Sciences, KU Leuven, Leuven, 3000, Belgium
Jonas Simons
Department of Earth and Environmental Sciences, KU Leuven, Leuven, 3000, Belgium
Stef Lhermitte
Department of Earth and Environmental Sciences, KU Leuven, Leuven, 3000, Belgium
Bart Muys
CORRESPONDING AUTHOR
Department of Earth and Environmental Sciences, KU Leuven, Leuven, 3000, Belgium
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Ann-Sofie P. Zinck, Bert Wouters, Franka Jesse, and Stef Lhermitte
EGUsphere, https://doi.org/10.5194/egusphere-2025-573, https://doi.org/10.5194/egusphere-2025-573, 2025
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Ocean-driven basal melting of ice shelves can carve channels into the ice shelf base. These channels represent potential weak areas of the ice shelf. On George VI Ice shelf we discover a new channel which onset coincides with the 2015 El-Nino Southern Oscillation event. Since the channel has developed rapidly and is located within a highly channelized area close to the ice shelf front it poses a potential thread of ice shelf retreat.
Weiran Li, Sanne B. M. Veldhuijsen, and Stef Lhermitte
The Cryosphere, 19, 37–61, https://doi.org/10.5194/tc-19-37-2025, https://doi.org/10.5194/tc-19-37-2025, 2025
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This study used a machine learning approach to estimate the densities over the Antarctic Ice Sheet, particularly in the areas where the snow is usually dry. The motivation is to establish a link between satellite parameters to snow densities, as measurements are difficult for people to take on site. It provides valuable insights into the complexities of the relationship between satellite parameters and firn density and provides potential for further studies.
Weiran Li, Stef Lhermitte, Bert Wouters, Cornelis Slobbe, Max Brils, and Xavier Fettweis
EGUsphere, https://doi.org/10.5194/egusphere-2024-3251, https://doi.org/10.5194/egusphere-2024-3251, 2024
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Due to the melt events in recent decades, the snow condition over Greenland has been changed. To observe this, we use a parameter (leading edge width; LeW) derived from satellite altimetry, and analyse its spatial and temporal variations. By comparing the LeW variations with modelled firn parameters, we concluded that the 2012 melt event has a long-lasting impact on the volume scattering of Greenland firn. This impact cannot fully recover due to the recent and more frequent melt events.
Julius Sommer, Maaike Izeboud, Sophie de Roda Husman, Bert Wouters, and Stef Lhermitte
EGUsphere, https://doi.org/10.5194/egusphere-2024-3105, https://doi.org/10.5194/egusphere-2024-3105, 2024
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Ice shelves, the floating extensions of Antarctica’s ice sheet, play a crucial role in preventing mass ice loss, and understanding their stability is crucial. If surface meltwater lakes drain rapidly through fractures, the ice shelf can destabilize. We analyzed satellite images of three years from the Shackleton Ice Shelf and found that lake drainages occurred in areas where damage is present and developing, and coincided with rising tides, offering insights into the drivers of this process.
Filippo Emilio Scarsi, Alessandro Battaglia, Maximilian Maahn, and Stef Lhermitte
EGUsphere, https://doi.org/10.5194/egusphere-2024-1917, https://doi.org/10.5194/egusphere-2024-1917, 2024
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Snowfall measurements at high latitudes are crucial for estimating ice sheet mass balance. Spaceborne radar and radiometer missions help estimate snowfall but face uncertainties. This work evaluates uncertainties in snowfall estimates from a fixed near-nadir radar (CloudSat-like) and a conically scanning radar (WIVERN-like), determining that WIVERN will provide much better estimates than CloudSat, and at much smaller spatial and temporal scales.
Thore Kausch, Stef Lhermitte, Marie G. P. Cavitte, Eric Keenan, and Shashwat Shukla
EGUsphere, https://doi.org/10.5194/egusphere-2024-2077, https://doi.org/10.5194/egusphere-2024-2077, 2024
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Determining the net balance of snow accumulation on the surface of Antarctica is challenging. Sentinel-1 satellite sensors, which can see through snow, offer a promising method. However, linking their signals to snow amounts is complex due to snow's internal structure and limited on-the-ground data. This study found a connection between satellite signals and snow levels at three locations in Dronning Maud Land. Using models and field data, the method shows potential for wider use in Antarctica.
Lena G. Buth, Valeria Di Biase, Peter Kuipers Munneke, Stef Lhermitte, Sanne B. M. Veldhuijsen, Sophie de Roda Husman, Michiel R. van den Broeke, and Bert Wouters
EGUsphere, https://doi.org/10.5194/egusphere-2023-2000, https://doi.org/10.5194/egusphere-2023-2000, 2023
Preprint archived
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Liquid meltwater which is stored in air bubbles in the compacted snow near the surface of Antarctica can affect ice shelf stability. In order to detect the presence of such firn aquifers over large scales, satellite remote sensing is needed. In this paper, we present our new detection method using radar satellite data as well as the results for the whole Antarctic Peninsula. Firn aquifers are found in the north and northwest of the peninsula, in agreement with locations predicted by models.
Ann-Sofie Priergaard Zinck, Bert Wouters, Erwin Lambert, and Stef Lhermitte
The Cryosphere, 17, 3785–3801, https://doi.org/10.5194/tc-17-3785-2023, https://doi.org/10.5194/tc-17-3785-2023, 2023
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The ice shelves in Antarctica are melting from below, which puts their stability at risk. Therefore, it is important to observe how much and where they are melting. In this study we use high-resolution satellite imagery to derive 50 m resolution basal melt rates of the Dotson Ice Shelf. With the high resolution of our product we are able to uncover small-scale features which may in the future help us to understand the state and fate of the Antarctic ice shelves and their (in)stability.
Diana Francis, Ricardo Fonseca, Kyle S. Mattingly, Stef Lhermitte, and Catherine Walker
The Cryosphere, 17, 3041–3062, https://doi.org/10.5194/tc-17-3041-2023, https://doi.org/10.5194/tc-17-3041-2023, 2023
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Role of Foehn Winds in ice and snow conditions at the Pine Island Glacier, West Antarctica.
Lena G. Buth, Bert Wouters, Sanne B. M. Veldhuijsen, Stef Lhermitte, Peter Kuipers Munneke, and Michiel R. van den Broeke
The Cryosphere Discuss., https://doi.org/10.5194/tc-2022-127, https://doi.org/10.5194/tc-2022-127, 2022
Manuscript not accepted for further review
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Liquid meltwater which is stored in air bubbles in the compacted snow near the surface of Antarctica can affect ice shelf stability. In order to detect the presence of such firn aquifers over large scales, satellite remote sensing is needed. In this paper, we present our new detection method using radar satellite data as well as the results for the whole Antarctic Peninsula. Firn aquifers are found in the north and northwest of the peninsula, in agreement with locations predicted by models.
Weiran Li, Cornelis Slobbe, and Stef Lhermitte
The Cryosphere, 16, 2225–2243, https://doi.org/10.5194/tc-16-2225-2022, https://doi.org/10.5194/tc-16-2225-2022, 2022
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This study proposes a new method for correcting the slope-induced errors in satellite radar altimetry. The slope-induced errors can significantly affect the height estimations of ice sheets if left uncorrected. This study applies the method to radar altimetry data (CryoSat-2) and compares the performance with two existing methods. The performance is assessed by comparison with independent height measurements from ICESat-2. The assessment shows that the method performs promisingly.
Zhongyang Hu, Peter Kuipers Munneke, Stef Lhermitte, Maaike Izeboud, and Michiel van den Broeke
The Cryosphere, 15, 5639–5658, https://doi.org/10.5194/tc-15-5639-2021, https://doi.org/10.5194/tc-15-5639-2021, 2021
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Antarctica is shrinking, and part of the mass loss is caused by higher temperatures leading to more snowmelt. We use computer models to estimate the amount of melt, but this can be inaccurate – specifically in the areas with the most melt. This is because the model cannot account for small, darker areas like rocks or darker ice. Thus, we trained a computer using artificial intelligence and satellite images that showed these darker areas. The model computed an improved estimate of melt.
Weiran Li, Stef Lhermitte, and Paco López-Dekker
The Cryosphere, 15, 5309–5322, https://doi.org/10.5194/tc-15-5309-2021, https://doi.org/10.5194/tc-15-5309-2021, 2021
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Surface meltwater lakes have been observed on several Antarctic ice shelves in field studies and optical images. Meltwater lakes can drain and refreeze, increasing the fragility of the ice shelves. The combination of synthetic aperture radar (SAR) backscatter and interferometric information (InSAR) can provide the cryosphere community with the possibility to continuously assess the dynamics of the meltwater lakes, potentially helping to facilitate the study of ice shelves in a changing climate.
Annelies Voordendag, Marion Réveillet, Shelley MacDonell, and Stef Lhermitte
The Cryosphere, 15, 4241–4259, https://doi.org/10.5194/tc-15-4241-2021, https://doi.org/10.5194/tc-15-4241-2021, 2021
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The sensitivity of two snow models (SNOWPACK and SnowModel) to various parameterizations and atmospheric forcing biases is assessed in the semi-arid Andes of Chile in winter 2017. Models show that sublimation is a main driver of ablation and that its relative contribution to total ablation is highly sensitive to the selected albedo parameterization and snow roughness length. The forcing and parameterizations are more important than the model choice, despite differences in physical complexity.
Diana Francis, Kyle S. Mattingly, Stef Lhermitte, Marouane Temimi, and Petra Heil
The Cryosphere, 15, 2147–2165, https://doi.org/10.5194/tc-15-2147-2021, https://doi.org/10.5194/tc-15-2147-2021, 2021
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The unexpected September 2019 calving event from the Amery Ice Shelf, the largest since 1963 and which occurred almost a decade earlier than expected, was triggered by atmospheric extremes. Explosive twin polar cyclones provided a deterministic role in this event by creating oceanward sea surface slope triggering the calving. The observed record-anomalous atmospheric conditions were promoted by blocking ridges and Antarctic-wide anomalous poleward transport of heat and moisture.
Christiaan T. van Dalum, Willem Jan van de Berg, Stef Lhermitte, and Michiel R. van den Broeke
The Cryosphere, 14, 3645–3662, https://doi.org/10.5194/tc-14-3645-2020, https://doi.org/10.5194/tc-14-3645-2020, 2020
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The reflectivity of sunlight, which is also known as albedo, is often inadequately modeled in regional climate models. Therefore, we have implemented a new snow and ice albedo scheme in the regional climate model RACMO2. In this study, we evaluate a new RACMO2 version for the Greenland ice sheet by using observations and the previous model version. RACMO2 output compares well with observations, and by including new processes we improve the ability of RACMO2 to make future climate projections.
Thore Kausch, Stef Lhermitte, Jan T. M. Lenaerts, Nander Wever, Mana Inoue, Frank Pattyn, Sainan Sun, Sarah Wauthy, Jean-Louis Tison, and Willem Jan van de Berg
The Cryosphere, 14, 3367–3380, https://doi.org/10.5194/tc-14-3367-2020, https://doi.org/10.5194/tc-14-3367-2020, 2020
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Ice rises are elevated parts of the otherwise flat ice shelf. Here we study the impact of an Antarctic ice rise on the surrounding snow accumulation by combining field data and modeling. Our results show a clear difference in average yearly snow accumulation between the windward side, the leeward side and the peak of the ice rise due to differences in snowfall and wind erosion. This is relevant for the interpretation of ice core records, which are often drilled on the peak of an ice rise.
Cited articles
Askar, Nuthammachot, N., Phairuang, W., Wicaksono, P., and Sayektiningsih, T.: Estimating Aboveground Biomass on Private Forest Using Sentinel-2 Imagery, J. Sensors, 6745629, 2018, https://doi.org/10.1155/2018/6745629, 2018.
Belgium: National Inventory Report (NIR), United Nations Framework Convention on Climate Change, https://unfccc.int/documents/224891 (last access: 20 November 2023), 2020.
Berben, J.: Dendrometrische studie van de Corsikaanse den, LISEC, Genk, 1983.
Bolar, K.: STAT: Interactive Document for Working with Basic Statistical Analysis, R package version 0.1.0, CRAN [code], https://CRAN.R-project.org/package=STAT (last access: 10 December 2024), 2019.
Brabantse Wouden: https://www.vlaamsbrabant.be/nl/natuur-en-milieu/brabantse-wouden, last access: 23 October 2023.
Chen, L., Wang, Y., Ren, C., Zhang, B., and Wang, Z.: Optimal Combination of Predictors and Algorithms for Forest Above-Ground Biomass Mapping from Sentinel and SRTM Data, Remote Sens.-Basel, 11, 414, https://doi.org/10.3390/rs11040414, 2019.
Dagnelie, P., Palm, R., Rondeux, J., and Thill, A.: Tables de cubage des arbres et des peuplements forestiers, Les presses agronomiques de Gembloux, ISBN 2-87016-062-3, 1985.
David, R. M., Rosser, N. J., and Donoghue, D. N. M.: Improving above ground biomass estimates of Southern Africa dryland forests by combining Sentinel-1 SAR and Sentinel-2 multispectral imagery, Remote Sens. Environ., 282, 113232, https://doi.org/10.1016/j.rse.2022.113232, 2022.
Dubayah, R., Blair, J. B., Goetz, S., Fatoyinbo, L., Hansen, M., Healey, S., Hofton, M., Hurtt, G., Kellner, J., Luthcke, S., Armston, J., Tang, H., Duncanson, L., Hancock, S., Jantz, P., Marselis, S., Patterson, P. L., Qi, W., and Silva, C.: The Global Ecosystem Dynamics Investigation: High-resolution laser ranging of the Earth’s forests and topography, Science of Remote Sensing, 1, 100002, https://doi.org/10.1016/j.srs.2020.100002, 2020.
Dugan, A. J., Birdsey, R., Healey, S. P., Pan, Y., Zhang, F., Mo, G., Chen, J., Woodall, C. W., Hernandez, A. J., McCullough, K., McCarter, J. B., Raymond, C. L., and Dante-Wood, K.: Forest sector carbon analyses support land management planning and projects: assessing the influence of anthropogenic and natural factors, Climatic Change, 144, 207–220, https://doi.org/10.1007/s10584-017-2038-5, 2017.
European Environment Agency: Forest Type 2018 (raster 100 m), Europe, 3-yearly, EEA [data set] https://doi.org/10.2909/db1af59f-f01f-4bd4-830c-f0eb652500c1, 2020.
Forkuor, G., Benewinde Zoungrana, J.-B., Dimobe, K., Ouattara, B., Vadrevu, K. P., and Tondoh, J. E.: Above-ground biomass mapping in West African dryland forest using Sentinel-1 and 2 datasets – A case study, Remote Sens. Environ., 236, 111496, https://doi.org/10.1016/j.rse.2019.111496, 2020.
Francini, S., D'Amico, G., Vangi, E., Borghi, C., and Chirici, G.: Integrating GEDI and Landsat: Spaceborne Lidar and Four Decades of Optical Imagery for the Analysis of Forest Disturbances and Biomass Changes in Italy, Sensors-Basel, 22, 2015, https://doi.org/10.3390/s22052015, 2022.
Garcia-Gonzalo, J., Peltola, H., Briceño-elizondo, E., and Kellomäki, S.: Changed thinning regimes may increase carbon stock under climate change: A case study from a Finnish boreal forest, Climatic Change, 81, 431–454, https://doi.org/10.1007/s10584-006-9149-8, 2007.
Goetz, S. J., Baccini, A., Laporte, N. T., Johns, T., Walker, W., Kellndorfer, J., Houghton, R. A., and Sun, M.: Mapping and monitoring carbon stocks with satellite observations: a comparison of methods, Carbon Balance Manag., 4, 2, https://doi.org/10.1186/1750-0680-4-2, 2009.
Goos, P.: Inleiding tot statistiek en kansrekenen, Acco, Leuven, 774, ISBN 9789463441858, 2017.
Gurung, M. B., Bigsby, H., Cullen, R., and Manandhar, U.: Estimation of carbon stock under different management regimes of tropical forest in the Terai Arc Landscape, Nepal, Forest Ecol. Manag., 356, 144–152, https://doi.org/10.1016/j.foreco.2015.07.024, 2015.
Hastie, T. and Tibshirani, R.: Generalized Additive Models, Stat. Sci., 1, 297–310, https://doi.org/10.1214/ss/1177013604, 1986.
Hoscilo, A., Aneta, L., Ziolkowski, D., Stereńczak, K., Lisańczuk, M., Schmullius, C., and Carsten, P.: Forest Aboveground Biomass Estimation Using a Combination of Sentinel-1 and Sentinel-2 Data, in: IGARSS 2018, IEEE International Geoscience and Remote Sensing Symposium, Valencia, Spain, 22–27 July 2018, 9026–9029, https://doi.org/10.1109/IGARSS.2018.8517965, 2018.
Huang, X., Ziniti, B., Torbick, N., and Ducey, M. J.: Assessment of Forest above Ground Biomass Estimation Using Multi-Temporal C-band Sentinel-1 and Polarimetric L-band PALSAR-2 Data, Remote Sens.-Basel, 10, 1424, https://doi.org/10.3390/rs10091424, 2018.
INFORMA: Science-based INtegrated FORest Mitigation mAnagement made operational for Europe: INFORMA Forest Management Platform, CORDIS – Eur. Comm., 46 pp., https://doi.org/10.3030/101060309, 2022.
IPCC: Good Practice Guidance for Land Use, Land-Use Change and Forestry. Institute for Global Environmental Strategies (IGES), Hayama, Japan, ISBN 4-88788-003-0, 2003.
Jandl, R., Lindner, M., Vesterdal, L., Bauwens, B., Baritz, R., Hagedorn, F., Johnson, D. W., Minkkinen, K., and Byrne, K. A.: How strongly can forest management influence soil carbon sequestration?, Geoderma, 137, 253–268, https://doi.org/10.1016/j.geoderma.2006.09.003, 2007.
Jiang, F., Deng, M., Tang, J., Fu, L., and Sun, H.: Integrating spaceborne LiDAR and Sentinel-2 images to estimate forest aboveground biomass in Northern China, Carbon Balance Manag., 17, 12, https://doi.org/10.1186/s13021-022-00212-y, 2022.
Jiao, Y., Wang, D., Yao, X., Wang, S., Chi, T., and Meng, Y.: Forest Emissions Reduction Assessment Using Optical Satellite Imagery and Space LiDAR Fusion for Carbon Stock Estimation, Remote Sens.-Basel, 15, 1410, https://doi.org/10.3390/rs15051410, 2023.
Kalies, E. L., Haubensak, K. A., and Finkral, A. J.: A meta-analysis of management effects on forest carbon storage, J. Sustain. Forest., 35, 311–323, https://doi.org/10.1080/10549811.2016.1154471, 2016.
Koch, B.: Status and future of laser scanning, synthetic aperture radar and hyperspectral remote sensing data for forest biomass assessment, ISPRS J. Photogramm., 65, 581–590, https://doi.org/10.1016/j.isprsjprs.2010.09.001, 2010.
Kun, Z., DellaSala, D., Keith, H., Kormos, C., Mercer, B., Moomaw, W. R., and Wiezik, M.: Recognizing the importance of unmanaged forests to mitigate climate change, GCB Bioenergy, 12, 1034–1035, https://doi.org/10.1111/gcbb.12714, 2020.
Kursa, M. and Rudnicki, W.: Feature Selection with Boruta Package, J. Stat. Softw., 36, 1–13, https://doi.org/10.18637/jss.v036.i11, 2010.
Lang, N., Schindler, K., and Wegner, J. D.: High carbon stock mapping at large scale with optical satellite imagery and spaceborne LIDAR, arXiv [preprint], https://doi.org/10.48550/arXiv.2107.07431, 15 July 2021.
Lang, N., Schindler, K., and Wegner, J. D.: Global canopy height map for the year 2020 derived from Sentinel-2 and GEDI, ETH Zurich [data set], https://doi.org/10.3929/ethz-b-000609802, 2022.
Lang, N., Jetz, W., and Wegner, J.: A high-resolution canopy height model of the Earth, Nature Ecology & Evolution, 7, 1–12, https://doi.org/10.1038/s41559-023-02206-6, 2023.
Laurin, G. V., Balling, J., Corona, P., Mattioli, W., Papale, D., Puletti, N., Rizzo, M., Truckenbrodt, J., and Urban, M.: Above-ground biomass prediction by Sentinel-1 multitemporal data in central Italy with integration of ALOS2 and Sentinel-2 data, J. Appl. Remote Sens., 12, 016008, https://doi.org/10.1117/1.JRS.12.016008, 2018.
Lu, D., Chen, Q., Wang, G., Moran, E., Batistella, M., Zhang, M., Vaglio Laurin, G., and Saah, D.: Aboveground Forest Biomass Estimation with Landsat and LiDAR Data and Uncertainty Analysis of the Estimates, Int. J. For. Res., 2012, 436537, https://doi.org/10.1155/2012/436537, 2012.
Luyssaert, S., Schulze, E.-D., Börner, A., Knohl, A., Hessenmöller, D., Law, B. E., Ciais, P., and Grace, J.: Old-growth forests as global carbon sinks, Nature, 455, 213–215, https://doi.org/10.1038/nature07276, 2008.
Melikov, C. H., Bukoski, J. J., Cook-Patton, S. C., Ban, H., Chen, J. L., and Potts, M. D.: Quantifying the Effect Size of Management Actions on Aboveground Carbon Stocks in Forest Plantations, Curr. For. Rep., 9, 131–148, https://doi.org/10.1007/s40725-023-00182-5, 2023.
Mikolāš, M., Piovesan, G., Ahlström, A., Donato, D. C., Gloor, R., Hofmeister, J., Keeton, W. S., Muys, B., Sabatini, F. M., Svoboda, M., and Kuemmerle, T.: Protect old-growth forests in Europe now, Science, 380, 466–466, https://doi.org/10.1126/science.adh2303, 2023.
Mngadi, M., Odindi, J., and Mutanga, O.: The Utility of Sentinel-2 Spectral Data in Quantifying Above-Ground Carbon Stock in an Urban Reforested Landscape, Remote Sens.-Basel, 13, 4281, https://doi.org/10.3390/rs13214281, 2021.
Moradi, F., Sadeghi, S. M. M., Heidarlou, H. B., Deljouei, A., Boshkar, E., and Borz, S. A.: Above-ground biomass estimation in a Mediterranean sparse coppice oak forest using Sentinel-2 data, Ann. For. Res., 65, 165–182, https://doi.org/10.15287/afr.2022.2390, 2022.
Morel, A. C. and Nogué, S.: Combining Contemporary and Paleoecological Perspectives for Estimating Forest Resilience, Front. For. Glob. Change, 2, 57, https://doi.org/10.3389/ffgc.2019.00057, 2019.
Musthafa, M. and Singh, G.: Improving Forest Above-Ground Biomass Retrieval Using Multi-Sensor L- and C-Band SAR Data and Multi-Temporal Spaceborne LiDAR Data, Front. For. Glob. Change, 5, 822704, https://doi.org/10.3389/ffgc.2022.822704, 2022.
Nadrowski, K., Wirth, C., and Scherer-Lorenzen, M.: Is forest diversity driving ecosystem function and service?, Curr. Opin. Env. Sust., 2, 75–79, https://doi.org/10.1016/j.cosust.2010.02.003, 2010.
Nuthammachot, N., Askar, A., Stratoulias, D., and Wicaksono, P.: Combined use of Sentinel-1 and Sentinel-2 data for improving above-ground biomass estimation, Geocarto Int., 37, 366–376, https://doi.org/10.1080/10106049.2020.1726507, 2022.
Perin, J., Bauwens, S., Pitchugin, M., Lejeune, P., and Hébert, J.: National Forestry Accounting Plan of Belgium, National Climate Commission, Belgium, 58 pp., https://www.cnc-nkc.be/sites/default/files/report/file/national_forestry_accounting_plan_-_belgium_-_18122019_1.pdf (last access: 20 November 2023), 2019.
Pires Coelho, A. J., Ribeiro Matos, F. A., Villa, P. M., Heringer, G., Pontara, V., de Paula Almado, R., and Alves Meira-Neto, J. A.: Multiple drivers influence tree species diversity and above-ground carbon stock in second-growth Atlantic forests: Implications for passive restoration, J. Environ. Manage., 318, 115588, https://doi.org/10.1016/j.jenvman.2022.115588, 2022.
Puliti, S., Hauglin, M., Breidenbach, J., Montesano, P., Neigh, C. S. R., Rahlf, J., Solberg, S., Klingenberg, T. F., and Astrup, R.: Modelling above-ground biomass stock over Norway using national forest inventory data with ArcticDEM and Sentinel-2 data, Remote Sens. Environ., 236, 111501, https://doi.org/10.1016/j.rse.2019.111501, 2020.
Quataert, P., Van der Aa, B., and Verschelde, P.: Opstellen van tarieven voro Inlandse eik en beuk in Vlaanderen ten behoeve van het berekenen van houtvolumes. Statistische evaluatie van de regressiemodellen en overzicht van de resultaten (technisch rapport deel III), Rapporten van het Instituut voor Natuuren Bosonderzoek 2011, 18, ISSN 1782-9054, https://publicaties.vlaanderen.be/view-file/9175 (last access: 20 November 2023), 2011.
Renaud, J. P., Sagar, A., Barbillon, P., Bouriaud, O., Deleuze, C., and Vega, C.: Characterizing the calibration domain of remote sensing models using convex hulls, Int. J. Appl. Earth Obs., 112, 102939, https://doi.org/10.1016/j.jag.2022.102939, 2022.
Rodríguez-Veiga, P., Wheeler, J., Louis, V., Tansey, K., and Balzter, H.: Quantifying Forest Biomass Carbon Stocks From Space, Curr. For. Rep., 3, 1–18, https://doi.org/10.1007/s40725-017-0052-5, 2017.
Rüetschi, M., Schaepman, M. E., and Small, D.: Using Multitemporal Sentinel-1 C-band Backscatter to Monitor Phenology and Classify Deciduous and Coniferous Forests in Northern Switzerland, Remote Sens.-Basel, 10, 55, https://doi.org/10.3390/rs10010055, 2018.
Ruiz-Peinado, R., Bravo-Oviedo, A., López-Senespleda, E., Bravo, F., and Río, M. D.: Forest management and carbon sequestration in the Mediterranean region: A review, For. Syst., 26, eR04S–eR04S, https://doi.org/10.5424/fs/2017262-11205, 2017.
Sabatini, F. M., de Andrade, R. B., Paillet, Y., Ódor, P., Bouget, C., Campagnaro, T., Gosselin, F., Janssen, P., Mattioli, W., Nascimbene, J., Sitzia, T., Kuemmerle, T., and Burrascano, S.: Trade-offs between carbon stocks and biodiversity in European temperate forests, Glob. Change Biol., 25, 536–548, https://doi.org/10.1111/gcb.14503, 2019.
Santoro, M., Beer, C., Cartus, O., Schmullius, C., Shvidenko, A., McCallum, I., Wegmüller, U., and Wiesmann, A.: Retrieval of growing stock volume in boreal forest using hyper-temporal series of Envisat ASAR ScanSAR backscatter measurements, Remote Sens. Environ., 115, 490–507, https://doi.org/10.1016/j.rse.2010.09.018, 2011.
Santoro, M., Cartus, O., Carvalhais, N., Rozendaal, D. M. A., Avitabile, V., Araza, A., de Bruin, S., Herold, M., Quegan, S., Rodríguez-Veiga, P., Balzter, H., Carreiras, J., Schepaschenko, D., Korets, M., Shimada, M., Itoh, T., Moreno Martínez, Á., Cavlovic, J., Cazzolla Gatti, R., da Conceição Bispo, P., Dewnath, N., Labrière, N., Liang, J., Lindsell, J., Mitchard, E. T. A., Morel, A., Pacheco Pascagaza, A. M., Ryan, C. M., Slik, F., Vaglio Laurin, G., Verbeeck, H., Wijaya, A., and Willcock, S.: The global forest above-ground biomass pool for 2010 estimated from high-resolution satellite observations, Earth Syst. Sci. Data, 13, 3927–3950, https://doi.org/10.5194/essd-13-3927-2021, 2021.
Schwehr, K.: Sentinel-2: Cloud Probability in Earth Engine, Version 1, Zenodo [data set], https://doi.org/10.5281/zenodo.7411046, 2020.
Sinha, S., Jeganathan, C., Sharma, L. K., and Nathawat, M. S.: A review of radar remote sensing for biomass estimation, Int. J. Environ. Sci. Te., 12, 1779–1792, https://doi.org/10.1007/s13762-015-0750-0, 2015.
Sun, X., Li, G., Wu, Q., Ruan, J., Li, D., and Lu, D.: Mapping Forest Carbon Stock Distribution in a Subtropical Region with the Integration of Airborne Lidar and Sentinel-2 Data, Remote Sens.-Basel, 16, 3847, https://doi.org/10.3390/rs16203847, 2024.
Thurner, M., Beer, C., Santoro, M., Carvalhais, N., Wutzler, T., Schepaschenko, D., Shvidenko, A., Kompter, E., Ahrens, B., Levick, S. R., and Schmullius, C.: Carbon stock and density of northern boreal and temperate forests, Global Ecol. Biogeogr., 23, 297–310, https://doi.org/10.1111/geb.12125, 2014.
Tian, L., Wu, X., Tao, Y., Li, M., Qian, C., Liao, L., and Fu, W.: Review of Remote Sensing-Based Methods for Forest Aboveground Biomass Estimation: Progress, Challenges, and Prospects, Forests, 14, 1086, https://doi.org/10.3390/f14061086, 2023.
Vanhellemont, M., Leyman, A., Govaere, L., De Keersmaeker, L., and Vandekerkhove, K.: Site-specific additionality in aboveground carbon sequestration in set-aside forests in Flanders (northern Belgium), Front. For. Glob. Change, 7, 1236203, https://doi.org/10.3389/ffgc.2024.1236203, 2024.
Van Winckel, S.: sofievanwinckel/RemoteSensing_CarbonManagement: Assessing the effect of forest management on above-ground carbon stock, v1.0, Zenodo [code and data set], https://doi.org/10.5281/zenodo.15631281, 2025.
Vayreda, J., Martinez-Vilalta, J., Gracia, M., and Retana, J.: Recent climate changes interact with stand structure and management to determine changes in tree carbon stocks in Spanish forests, Glob. Change Biol., 18, 1028–1041, https://doi.org/10.1111/j.1365-2486.2011.02606.x, 2012.
Wood, S. N.: Generalized Additive Models: An Introduction with R, Chapman & Hall/CRC, Boca Raton, 392 pp., https://doi.org/10.1201/9781315370279, 2006.
Xiao, J., Chevallier, F., Gomez, C., Guanter, L., Hicke, J. A., Huete, A. R., Ichii, K., Ni, W., Pang, Y., Rahman, A. F., Sun, G., Yuan, W., Zhang, L., and Zhang, X.: Remote sensing of the terrestrial carbon cycle: A review of advances over 50 years, Remote Sens. Environ., 233, 111383, https://doi.org/10.1016/j.rse.2019.111383, 2019.
Zhang, H., Zhang, Z., Liu, K., Huang, C., and Dong, G.: Integrating land use management with trade-offs between ecosystem services: A framework and application, Ecol. Indic., 149, 110193, https://doi.org/10.1016/j.ecolind.2023.110193, 2023.
Zianis, D., Muukkonen, P., Mäkipää, R., and Mencuccini, M.: Biomass and stem volume equations for tree species in Europe, Silva Fenn. Monographs, 4, 1–2, 5–63, ISBN 951-40-1983-0, 2005.
Short summary
Insights on management's impact on forest carbon stocks are crucial for sustainable forest management practices. However, accurately monitoring carbon stocks remains a technological challenge. This study estimates above-ground carbon stock in managed and unmanaged forests using passive optical, synthetic aperture radar (SAR), and light detection and ranging (lidar) remote sensing data. Results show promising potential in using multiple remote sensing predictors and publicly available high-resolution data for mapping forest carbon stocks.
Insights on management's impact on forest carbon stocks are crucial for sustainable forest...
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