| Van de Kerchove, Ruben |
ORCID
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| Previous institute |
Top | Publications |
Vlaamse overheid; Beleidsdomein Economie, Wetenschap en Innovatie; Vlaamse Instelling voor Technologisch Onderzoek (VITO), more
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| Publications (10) |
Top | Publications |
| A1 Publications (7) [show] |
- Lucas, R.; Otero, V.; Van De Kerchove, R.; Lagomasino, D.; Satyanarayana, B.; Fatoyinbo, T.; Dahdouh-Guebas, F. (2021). Monitoring Matang's mangroves in peninsular Malaysia through earth observations: a globally relevant approach. Land Degradation & Development 32(1): 354-373. https://hdl.handle.net/10.1002/ldr.3652, more
- Ewald, M.; Skowronek, S.; Aerts, R.; Lenoir, J.; Feilhauer, H.; Van De Kerchove, R.; Honnay, O.; Somers, B.; Garzón-López, C.X.; Rocchini, D.; Schmidtlein, S. (2020). Assessing the impact of an invasive bryophyte on plant species richness using high resolution imaging spectroscopy. Ecol. Indic. 110: 105882. https://dx.doi.org/10.1016/j.ecolind.2019.105882, more
 Lucas, R.; Van De Kerchove, R.; Otero, V.; Lagomasino, D.; Fatoyinbo, L.; Omar, H.; Satyanarayana, B.; Dahdouh-Guebas, F. (2020). Structural characterisation of mangrove forests achieved through combining multiple sources of remote sensing data. Remote Sens. Environ. 237: 111543. https://dx.doi.org/10.1016/j.rse.2019.111543, more- Otero, V.; Lucas, R.; Van De Kerchove, R.; Satyanarayana, B.; Mohd-Lokman, H.; Dahdouh-Guebas, F. (2020). Spatial analysis of early mangrove regeneration in the Matang Mangrove Forest Reserve, Peninsular Malaysia, using geomatics. Forest Ecol. Manag. 472: 118213. https://hdl.handle.net/10.1016/j.foreco.2020.118213, more
- Otero, V.; Van De Kerchove, R.; Satyanarayana, B.; Mohd-Lokman, H.; Lucas, R.; Dahdouh-Guebas, F. (2019). An analysis of the early regeneration of mangrove forests using Landsat time series in the Matang Mangrove Forest Reserve, Peninsular Malaysia. Remote Sens. 11(7): 774. https://dx.doi.org/10.3390/rs11070774, more
- Unberath, I.; Vanierschot, L.; Somers, B.; Van De Kerchove, R.; Vanden Borre, J.; Unberath, M.; Feilhauer, H. (2019). Remote sensing of coastal vegetation: dealing with high species turnover by mapping multiple floristic gradients. Applied Vegetation Science 22(4): 534-546. https://dx.doi.org/10.1111/avsc.12446, more
- Otero, V.; Van De Kerchove, R.; Satyanarayana, B.; Martínez-Espinosa, C.; Fisol, M.A.B.; Ibrahim, M.R.B.; Sulong, I.; Mohd-Lokman, H.; Lucas, R.; Dahdouh-Guebas, F. (2018). Managing mangrove forests from the sky: forest inventory using field data and Unmanned Aerial Vehicle (UAV) imagery in the Matang Mangrove Forest Reserve, peninsular Malaysia. Forest Ecol. Manag. 411: 35-45. https://dx.doi.org/10.1016/j.foreco.2017.12.049, more
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| Peer reviewed publication [show] |
- Adriaensen, S.; Sterckx, S.; De Keukelaere, L.; Van de Kerchove, R.; Knaeps, E. (2018). Atmospheric correction ICOR and integration in operational workflows, in: 38th IEEE International Geoscience and Remote Sensing Symposium (IGARSS). IEEE International Symposium on Geoscience and Remote Sensing IGARSS, 38: pp. 3524-3526. https://dx.doi.org/10.1109/IGARSS.2018.8518044, more
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| Book chapter [show] |
- Otero, V.; Martinez-Espinosa, C.; Dahdouh-Guebas, F.; Van De Kerchove, R.; Satyanarayana, B.; Lucas, R. (2017). Variations in mangrove regeneration rates under different management plans: An analysis of Landsat time-series in the Matang Mangrove Forest Reserve, Peninsular Malaysia, in: 2017 9th International Workshop on the Analysis of Multitemporal Remote Sensing Images (MultiTemp). pp. 3. https://dx.doi.org/10.1109/Multi-Temp.2017.8035238, more
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| Other publication [show] |
- Vanderhoeven, S.; Adriaens, T.; Desmet, P.; Strubbe, D.; Backeljau, T.; Barbier, Y.; Brosens, D.; Cigar, J.; Coupremanne, M.; De Troch, R.; Eggermont, H.; Heughebaert, A.; Hostens, K.; Huybrechts, P.; Jacquemart, A.-L.; Lens, L.; Monty, A.; Paquet, J.-Y.; Prévot, C.; Robertson, T.; Termonia, P.; Van De Kerchove, R.; Van Hoey, G.; Van Schaeybroeck, B.; Vercayie, D.; Verleye, T.J.; Welby, S.; Groom, Q.J. (2017). Tracking Invasive Alien Species (TrIAS): building a data-driven framework to inform policy. Research Ideas and Outcomes 3: e13414. https://dx.doi.org/10.3897/rio.3.e13414, more
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