Mean height and variability of height derived from Lidar data and Landsat images relationship

Pascual Castaño, Isabel Cristina; Cohen, Warren; García Abril, Antonio; Arroyo Méndez, Lara Ainoa; Valbuena Puebla, Ruben; Martí Fernández, Susana y Manzanera de la Vega, José Antonio (2008). Mean height and variability of height derived from Lidar data and Landsat images relationship. En: "SilviLaser 2008, 8th International Conference on LiDAR Applications in Forest assessment and Inventory. Sept. 17-19, 2008", 17/09/2008-19/09/2008, Edinburgh, UK. ISBN 978-0-85538-774-7. pp. 517-525.

Descripción

Título: Mean height and variability of height derived from Lidar data and Landsat images relationship
Autor/es:
  • Pascual Castaño, Isabel Cristina
  • Cohen, Warren
  • García Abril, Antonio
  • Arroyo Méndez, Lara Ainoa
  • Valbuena Puebla, Ruben
  • Martí Fernández, Susana
  • Manzanera de la Vega, José Antonio
Tipo de Documento: Ponencia en Congreso o Jornada (Artículo)
Título del Evento: SilviLaser 2008, 8th International Conference on LiDAR Applications in Forest assessment and Inventory. Sept. 17-19, 2008
Fechas del Evento: 17/09/2008-19/09/2008
Lugar del Evento: Edinburgh, UK
Título del Libro: Proceedings of SilviLaser 2008: 8th International Conference on LiDAR Applications in Forest assessment and Inventory
Fecha: 2008
ISBN: 978-0-85538-774-7
Materias:
Palabras Clave Informales: Lidar, Landsat, mean height, Forest structure
Escuela: E.T.S.I. Montes (UPM) [antigua denominación]
Departamento: Silvopascicultura [hasta 2014]
Licencias Creative Commons: Reconocimiento - Sin obra derivada - No comercial

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Resumen

The mean height and standard deviat ion of the height of the forest canopy, derived from lidar data show to be important variables to summarize forest st ructure. However lidar data has a limited spat ial extent and very high economic cost . Landsat data provide useful st ructural informat ion in the horizontal plane and have easy access. The integrat ion of both data sources is an interest ing goal for sustainable forest management. Different spect ral indices (NDVI and Tasseled Cap) were obtained from 3 Landsat scenes (March 2000, June 2001 and September 2001). In addit ion, mean and standard deviat ion of lidar height werecalculated in 30x30m blocks. Correlat ion and forward stepwise regression analysis was applied between these two variables sets. Best correlat ion coefficients are achieved among mean lidar height versus NDVI and wetness for the three dates (range between 0.65 to -0.73). Others authors indicate that wetness is one of the best spectral indices to characterize forest st ructure. Best regression models include NDVI and wetness of June and September as dependent variables (adjusted r2: 0.55 – 0.62). These results show that lidar data can be useful for training Landsat to map forest st ructure but it should be interest ing to opt imize this approach.

Más información

ID de Registro: 3107
Identificador DC: http://oa.upm.es/3107/
Identificador OAI: oai:oa.upm.es:3107
URL Oficial: http://geography.swan.ac.uk/silvilaser/papers/poster_papers/Pascual.pdf
Depositado por: Memoria Investigacion
Depositado el: 01 Jun 2010 09:32
Ultima Modificación: 20 Abr 2016 12:39
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