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  5. Estimation of Mediterranean forest attributes by the application of k-NN procedures to multitemporal Landsat ETM+ images

Estimation of Mediterranean forest attributes by the application of k-NN procedures to multitemporal Landsat ETM+ images

Author(s)
Maselli, Fabio
Chirici, Gherardo
Bottai, Lorenzo
Corona, Piermaria
Marchetti, Marco
Date Issued
2005
Type
Article
DOI
10.1080/01431160500166433
Abstract
Routinely, applications of nonparametric estimation methods to satellite data for assisting the creation of forest inventories in Northern European countries are stimulating interest in the possible extension of these methods to more complex Mediterranean areas. This is the subject of the current work, which presents an experiment based on the integration of remotely sensed images and sample field measurements aimed at producing forest attribute maps in central Italy. Testing was carried out in an area where 370 geocoded field plots, sampled on a singlestage cluster design, were collected to characterize wood and non-wood forest attributes. These ground data served to apply various k-Nearest Neighbour (k- NN) estimation procedures to multitemporal Landsat 7 ETM+ images in order to map major forest attributes (basal area and simulated leaf area index, LAI). More specifically, the investigation focused on evaluating the effects of using satellite images from different periods of the growing season and spectral metrics of increasing complexity. The results achieved by the examined methods are finally discussed in order to provide guidelines for possible operational utilization.
Additional information
L'articolo รจ disponibile sul sito dell'editore http://www.tandf.co.uk/journals/
Citation
Maselli, F. et al. 2005. Estimation of Mediterranean forest attributes by the application of k-NN procedures to multitemporal Landsat ETM+ images. "International Journal of Remote Sensing " 26 (17): 3781-3796.
Subjects

k-Nearest Neighbour

Forest inventories

Mediterranean areas

Landsat 7 ETM+

Handle
http://hdl.handle.net/2067/2135
File(s)
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maselli_KNN_IJRS1.pdf

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51.14 KB

Format

Adobe PDF

Checksum (MD5)

825e6607a857d1ed448d711e86366fdb

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