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

Pascual Castaño, Isabel Cristina and Cohen, Warren and García Abril, Antonio and Arroyo Méndez, Lara Ainoa and Valbuena Puebla, Ruben and Martí Fernández, Susana and Manzanera de la Vega, José Antonio (2008). Mean height and variability of height derived from Lidar data and Landsat images relationship. In: "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.

Description

Title: Mean height and variability of height derived from Lidar data and Landsat images relationship
Author/s:
  • 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
Item Type: Presentation at Congress or Conference (Article)
Event Title: SilviLaser 2008, 8th International Conference on LiDAR Applications in Forest assessment and Inventory. Sept. 17-19, 2008
Event Dates: 17/09/2008-19/09/2008
Event Location: Edinburgh, UK
Title of Book: Proceedings of SilviLaser 2008: 8th International Conference on LiDAR Applications in Forest assessment and Inventory
Date: 2008
ISBN: 978-0-85538-774-7
Subjects:
Freetext Keywords: Lidar, Landsat, mean height, Forest structure
Faculty: E.T.S.I. Montes (UPM)
Department: Silvopascicultura [hasta 2014]
Creative Commons Licenses: Recognition - No derivative works - Non commercial

Full text

[img]
Preview
PDF - Requires a PDF viewer, such as GSview, Xpdf or Adobe Acrobat Reader
Download (719kB) | Preview

Abstract

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.

More information

Item ID: 3107
DC Identifier: http://oa.upm.es/3107/
OAI Identifier: oai:oa.upm.es:3107
Official URL: http://geography.swan.ac.uk/silvilaser/papers/poster_papers/Pascual.pdf
Deposited by: Memoria Investigacion
Deposited on: 01 Jun 2010 09:32
Last Modified: 20 Apr 2016 12:39
  • Logo InvestigaM (UPM)
  • Logo GEOUP4
  • Logo Open Access
  • Open Access
  • Logo Sherpa/Romeo
    Check whether the anglo-saxon journal in which you have published an article allows you to also publish it under open access.
  • Logo Dulcinea
    Check whether the spanish journal in which you have published an article allows you to also publish it under open access.
  • Logo de Recolecta
  • Logo del Observatorio I+D+i UPM
  • Logo de OpenCourseWare UPM