Forest species mapping using airborne hyperspectral APEX data
References
- Alonzo, M, Bookhagen, B & Roberts, DA 2014, ‘Urban tree species mapping using hyperspectral and LiDAR data fusion’, Remote Sensing of Environment, vol. 148, pp. 70-83.
- Baldeck, CA, Asner, GP, Martin, RE, Anderson, CB, Knapp, DE, Kellner, JR & Wright, SJ 2015, ‘Operational Tree Species Mapping in a Diverse Tropical Forest with Airborne Imaging Spectroscopy’, PLOS ONE, vol. 10, no. 7, pp. 1-21.
- Boschetti, M, Boschetti, L, Oliveri, S, Casati, L & Canova, I 2007, ‘Tree species mapping with airborne hyperspectral MIVIS data: the Ticino Park study case’, International Journal of Remote Sensing, vol. 28, no. 6, pp. 1251-1261.
- Cho, MA, Mathieu, R, Asner, GP, Naidoo, L, van Aardt, J, Ramoelo, A, Debba, P, Wessels, K, Main, R, Smit, IPJ & Erasmus, B 2012, ‘Mapping tree species composition in South African savannas using an integrated airborne spectral and LiDAR system’, Remote Sensing of Environment, vol. 125, pp. 214-226.
- Clark, ML, Roberts, DA & Clark, DB 2005, ‘Hyperspectral discrimination of tropical rain forest tree species at leaf to crown scales’, Remote Sensing of Environment, vol. 96, pp. 375-398.
- Colgan, M, Baldeck, C, Féret, JB & Asner, GP 2012, ‘Mapping Savanna Tree Species at Ecosystem Scales Using Support Vector Machine Classification and BRDF Correction on Airborne Hyperspectral and LiDAR Data’, Remote Sensing, vol. 4, no. 11, pp. 3462-3480.
- Dalponte, M, Bruzzone, L & Gianelle, D 2008, ‘Fusion of Hyperspectral and LiDAR Remote Sensing Data for Classification of Complex Forest Areas’, IEEE Transactions on Geoscience and Remote Sensing, vol. 46, no. 5, pp. 1416-1427.
- Dalponte, M, Bruzzone, L & Gianelle, D 2012, ‘Tree species classification in the Southern Alps based on the fusion of very high geometrical resolution multispectral/hyperspectral images and LiDAR data’, Remote Sensing of Environment, vol. 123, pp. 258-270.
- Dalponte, M, Orka, HO, Gobakken, T, Gianelle, D & Naesset, E 2013, ‘Tree Species Classification in Boreal Forests With Hyperspectral Data’, IEEE Transactions on Geoscience and Remote Sensing, vol. 51, no. 5, pp. 2632-2645.
- Filella, I & Penuelas, J 1994, ‘The red edge position and shape as indicators of plant chlorophyll content, biomass and hydric status’, International Journal of Remote Sensing, vol. 15, no. 7, pp. 1459-1470.
- Franklin, SE 2001, Remote Sensing for Sustainable Forest Management, CRC Press, Boca Raton.
- Gao, BC, Montes, MJ & Davis, CO 2004, ‘Refinement of wavelength calibrations of hyperspectral imaging data using a spectrum-matching technique’, Remote Sensing of Environment, vol. 90, no. 4, pp. 424-433.
- Ghosh, A, Fassnacht, FE, Joshi, PK & Koch, B 2014, ‘A framework for mapping tree species combining hyperspectral and LiDAR data: Role of selected classifiers and sensor across three spatial scales’, International Journal of Applied Earth Observation and Geoinformation, vol. 26, pp. 49-63.
- Horler, DNH, Dockray, M & Barber, J 1983, ‘The red edge of plant leaf reflectance’, International Journal of Remote Sensing, vol. 4, no. 2, pp. 273-288.
- Hu, B, Miller, JR, Zarco-Tejada, PJ, Freemantle, J & Zwick, H 2008, ‘Boreal forest mapping at the BOREAS study area using seasonal optical indices sensitive to plant pigment content’, Canadian Journal of Remote Sensing, vol. 34, pp. 158-171.
- Hughes, G 1968, ‘On the mean accuracy of statistical pattern recognizers’, IEEE Transactions on Information Theory, vol. 14, no. 1, pp. 55-63.
- Jones, TG, Coops, NC, & Sharma, T 2010, ‘Assessing the utility of airborne hyperspectral and LiDAR data for species distribution mapping in the coastal Pacific Northwest, Canada’, Remote Sensing of Environment, vol. 114, no. 12, pp. 2841-2852.
- Kempeneers, P, Van Coillie, F, Liao, W, Devriendt, F & Vandekerkhove, K 2014, ‘Tree species mapping by combining hyperspectral with LiDAR data’, Proceedings of the IEEE International Geoscience and Remote Sensing Symposium, Québec.
- Linke, J, Betts, MG, Lavigne, MB & Franklin, SE 2006, ‘Introduction: structure, function and change of forest landscapes’ in Understanding forest disturbance and spatial pattern: Remote sensing and GIS approaches, eds. M Wulder & SE Franklin, Taylor & Francis Group, Abingdon, pp. 1-29.
- Marcinkowska, A, Zagajewski, B, Ochtyra, A, Jarocińska, A, Raczko, E, Kupková, L, Stych, P & Meuleman, K 2014, ‘Mapping vegetation communities of the Karkonosze National Park using APEX hyperspectral data and Support Vector Machines’, Miscellanea Geographica, vol. 18, no. 2, pp. 23-29.
- Pandey, PC, Tate, NJ & Balzter, H 2014, ‘Mapping Tree Species in Coastal Portugal Using Statistically Segmented Principal Component Analysis and Other Methods’, IEEE Sensors Journal, vol. 14, no. 12, pp. 4434-4441.
- Panigada, C, Rossini, M, Busetto, L, Meroni, M, Fava, F & Colombo, R 2010, ‘Chlorophyll concentration mapping with MIVIS data to assess crown discoloration in the Ticino Park oak forest’, International Journal of Remote Sensing, vol. 31, no. 12, pp. 3307-3332.
- Zarco-Tejada, PJ & Miller, JR 1999, ‘Land cover mapping at BOREAS using red edge spectral parameters from CASI imagery’, Journal of Geophysical Research, vol. 104, no. D22, pp. 27921-27933.
DOI: https://doi.org/10.1515/mgrsd-2016-0002 | Journal eISSN: 2084-6118 | Journal ISSN: 0867-6046 (formerly 2084-6118)
Language: English
Page range: 28 - 33
Submitted on: Jun 10, 2015
Accepted on: Dec 15, 2015
Published on: Apr 20, 2016
Published by: Faculty of Geography and Regional Studies, University of Warsaw
In partnership with: Paradigm Publishing Services
Keywords:
Related subjects:
© 2016 Giulia Tagliabue, Cinzia Panigada, Roberto Colombo, Francesco Fava, Chiara Cilia, Frédéric Baret, Kristin Vreys, Koen Meuleman, Micol Rossini, published by Faculty of Geography and Regional Studies, University of Warsaw
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.