# Publications

Updated April 2023

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[1] I. Aalto, J. Aalto, S. Hancock, et al. “Quantifying the impact of management on the three-dimensional structure of boreal forests”. In: Forest Ecology and Management 535 (2023), p. 120885. DOI: https://doi.org/10.1016/j.foreco.2023.120885.

[2] G. Vincent, P. Verley, B. Brede, et al. “Multi-sensor airborne lidar requires intercalibration for consistent estimation of light attenuation and plant area density”. In: Remote Sensing of Environment 286 (2023), p. 113442. ISSN: 0034-4257. DOI: https://doi.org/10.1016/j.rse.2022.113442.

[3] K. R. Dayal, S. Durrieu, K. Lahssini, et al. “An investigation into lidar scan angle impacts on stand attribute predictions in different forest environments”. In: ISPRS Journal of Photogrammetry and Remote Sensing 193 (2022), pp. 314-338. DOI: https://doi.org/10.1016/j.isprsjprs.2022.08.013.

[4] E. E. Maeda, M. H. Nunes, K. Calders, et al. “Shifts in structural diversity of Amazonian forest edges detected using terrestrial laser scanning”. In: Remote Sensing of Environment 271 (2022), p. 112895. DOI: https://doi.org/10.1016/j.rse.2022.112895.

[5] M. H. Nunes, J. L. C. Camargo, G. Vincent, et al. “Forest fragmentation impacts the seasonality of Amazonian evergreen canopies”. In: Nature communications 13.1 (2022), pp. 1-10. DOI: https://doi.org/10.1038/s41467-022-28490-7.

[6] E. G. Santos, M. H. Nunes, T. Jackson, et al. “Quantifying tropical forest disturbances using canopy structural traits derived from terrestrial laser scanning”. In: Forest Ecology and Management 524 (2022), p. 120546. DOI: https://doi.org/10.1016/j.foreco.2022.120546.

[7] H. Weiser, J. Schäfer, L. Winiwarter, et al. “Individual tree point clouds and tree measurements from multi-platform laser scanning in German forests”. In: Earth System Science Data 14.7 (2022), pp. 2989-3012. DOI: https://doi.org/10.5194/essd-14-2989-2022.

[8] L. Winiwarter, A. M. E. Pena, H. Weiser, et al. “Virtual laser scanning with HELIOS++: A novel take on ray tracing-based simulation of topographic full-waveform 3D laser scanning”. In: Remote Sensing of Environment 269 (2022), p. 112772. DOI: https://doi.org/10.1016/j.rse.2021.112772.

[9] N. Barbier, J. Ball, I. Clocher, et al. “Sensing Tropical Forest Phenology and Productivity from the Field to the Satellite”. In: 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS. IEEE. 2021, pp. 716-719. DOI: https://doi.org/10.1109/IGARSS47720.2021.9554372.

[10] A. Damm, E. Paul-Limoges, D. Kükenbrink, et al. “Remote sensing of forest gas exchange: Considerations derived from a tomographic perspective”. In: Global Change Biology 26.4 (2020), pp. 2717-2727. DOI: https://doi.org/10.1111/gcb.15007.

[11] A. Deasey. “How drought, waterlogging, and light availability shape patterns of tropical tree distributions, in French Guiana”. PhD thesis. University of Stirling, 2020. http://hdl.handle.net/1893/31957.

[12] D. Kükenbrink, F. D. Schneider, B. Schmid, et al. “Modelling of three-dimensional, diurnal light extinction in two contrasting forests”. In: Agricultural and Forest Meteorology (2020), p. 108230. DOI: https://doi.org/10.1016/j.agrformet.2020.108230.

[13] S. Pimmasarn, N. K. Tripathi, S. Ninsawat, et al. “Applying LiDAR to Quantify the Plant Area Index Along a Successional Gradient in a Tropical Forest of Thailand”. In: Forests 11.5 (2020), p. 520. DOI: https://doi.org/10.3390/f11050520.

[14] D. M. Ebengo, F. de Boissieu, C. Lavalley, et al. “Simulating Spectral Heterogeneity In Tropical Forest Canopy Reflectance With 3d Radiative Transfer Modeling”. In: 2019 10th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing (WHISPERS). IEEE. 2019, pp. 1-5. DOI: https://doi.org/10.1109/WHISPERS.2019.8920985.

[15] D. Kükenbrink, A. Hueni, F. D. Schneider, et al. “Mapping the irradiance field of a single tree: quantifying vegetation-induced adjacency effects”. In: IEEE Transactions on Geoscience and Remote Sensing 57.7 (2019), pp. 4994-5011. DOI: https://doi.org/10.1109/TGRS.2019.2895211.

[16] F. D. Schneider, D. Kükenbrink, M. E. Schaepman, et al. “Quantifying 3D structure and occlusion in dense tropical and temperate forests using close-range LiDAR”. In: Agricultural and Forest Meteorology 268 (2019), pp. 249-257. DOI: https://doi.org/10.1016/j.agrformet.2019.01.033.

[17] R. B. Sinou. “Validation of the simulation of LIDAR signals with DART for the LEAF-EXPEVAL Project”. PhD thesis. Mastère SPécialisé SAPS® Space Applications & Services-ISAE-SUPAERO 2018/2019, 2019. https://hal.inrae.fr/hal-02609789.

[18] B. Tymen, G. Vincent, E. A. Courtois, et al. “Quantifying micro-environmental variation in tropical rainforest understory at landscape scale by combining airborne LiDAR scanning and a sensor network”. In: Annals of Forest Science 74.2 (2017), p. 32. DOI: https://doi.org/10.1007/s13595-017-0628-z.

[19] G. Vincent, C. Antin, M. Laurans, et al. “Mapping plant area index of tropical evergreen forest by airborne laser scanning. A cross-validation study using LAI2200 optical sensor”. In: Remote Sensing of Environment 198 (2017), pp. 254-266. DOI: https://doi.org/10.1016/j.rse.2017.05.034.

[20] E. A. Cherrington. “Towards ecologically consistent remote sensing mapping of tree communities in French Guiana”. PhD thesis. AgroParisTech-ENGREF; Technische Universität Dresden, 2016. https://theses.hal.science/tel-01486533/.

[21] J. Dauzat, C. Madelaine-Antin, J. Heurtebize, et al. “How Much Commercial Timber in Your Plot, How Much Carbon Sequestrated in the Trees, How Much Light Available for Undercrops? Terrestrial LIDAR is the Right Technology For Addressing These Questions”. In: 3rd European Agroforestry Conference-Book of Abstracts; Gosme, M., Ed. 2016, pp. 121-124. https://agritrop.cirad.fr/580646/.