Advances in forest characterisation, mapping and monitoring through integration of LiDAR and other remote sensing datasets

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dc.contributor.author Lee, Alex
dc.contributor.author Carreiras, Joao
dc.contributor.author Bunting, Peter J.
dc.contributor.author Breyer, Johanna
dc.contributor.author Armston, John
dc.contributor.author Lucas, Richard M.
dc.date.accessioned 2010-10-11T11:22:37Z
dc.date.available 2010-10-11T11:22:37Z
dc.date.issued 2008
dc.identifier.citation Lee , A , Carreiras , J , Bunting , P J , Breyer , J , Armston , J & Lucas , R M 2008 , ' Advances in forest characterisation, mapping and monitoring through integration of LiDAR and other remote sensing datasets ' pp. 17-19 . en
dc.identifier.other PURE: 151908
dc.identifier.other dspace: 2160/5770
dc.identifier.uri http://hdl.handle.net/2160/5770
dc.identifier.uri http://geography.swan.ac.uk/silvilaser/papers/keynote_papers/Lucas.pdf en
dc.description Lucas RM, Lee A, Armston J, Breyer J, Bunting P, Carreiras J. 2008. Advances in forest characterisation, mapping and monitoring through integration of LiDAR and other remote sensing datasets. Silvilaser 2008: 8th international conference on LiDAR applications in forest assessment and inventory, SilviLaser 2008, September 17-19, 2008 – Edinburgh, UK en
dc.description.abstract The diversity of scales and modes in which ground, airborne and spaceborne LiDAR operate has increased opportunities for quantitatively assessing forest structure, biomass and species composition and obtaining more general information on dynamics and ecological/commercial value. However, the level of information extracted can be increased even further by integrating data from other sensor types, including hyperspectral and Synthetic Aperture Radar (SAR). Examples include the generation of species-specific tree and stand level maps of biomass through inclusion of fine spatial resolution hyperspectral data and the use of LiDAR data and derived products for better interpreting the information content of SAR and optical data and parameterising models that simulate and assist understanding of the interaction of electromagnetic energy with forest components. Applications where synergistic use of LiDAR and other remote sensing data are advantageous include commercial forest inventory, quantifying carbon dynamics and biodiversity, and detecting change at scales from individual trees to landscapes. Recognition of the value of integrating other forms of remote sensing data with LiDAR is leading to the development of techniques for data fusion and also new synergistic sensors on platforms ranging from Unmanned Airborne Vehicles (UAVs) to satellites (e.g., DESDynI). en
dc.format.extent 3 en
dc.language.iso eng
dc.relation.ispartof en
dc.title Advances in forest characterisation, mapping and monitoring through integration of LiDAR and other remote sensing datasets en
dc.type Text en
dc.type.publicationtype Conference contribution en
dc.contributor.institution Institute of Geography & Earth Sciences en
dc.contributor.institution Quaternary Environmental Change Group en
dc.description.status Non peer reviewed en


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