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  <title>Unitus DSpace</title>
  <link rel="alternate" href="http://http://dspace.unitus.it:80" />
  <subtitle>The DSpace digital repository system captures, stores, indexes, preserves, and distributes digital research material.</subtitle>
  <id>http://http://dspace.unitus.it:80</id>
  <updated>2013-05-18T20:04:59Z</updated>
  <dc:date>2013-05-18T20:04:59Z</dc:date>
  <entry>
    <title>Estimation of Mediterranean forest attributes by the application of k-NN procedures to multitemporal Landsat ETM+ images</title>
    <link rel="alternate" href="http://hdl.handle.net/2067/2135" />
    <author>
      <name>Maselli, Fabio</name>
    </author>
    <author>
      <name>Chirici, Gherardo</name>
    </author>
    <author>
      <name>Bottai, Lorenzo</name>
    </author>
    <author>
      <name>Corona, Piermaria</name>
    </author>
    <author>
      <name>Marchetti, Marco</name>
    </author>
    <id>http://hdl.handle.net/2067/2135</id>
    <updated>2011-06-14T00:30:55Z</updated>
    <published>2004-12-31T23:00:00Z</published>
    <summary type="text">Title: Estimation of Mediterranean forest attributes by the application of k-NN procedures to multitemporal Landsat ETM+ images
Authors: Maselli, Fabio; Chirici, Gherardo; Bottai, Lorenzo; Corona, Piermaria; Marchetti, Marco
Abstract: Routinely, applications of nonparametric estimation methods to satellite data for&#xD;
assisting the creation of forest inventories in Northern European countries are&#xD;
stimulating interest in the possible extension of these methods to more complex&#xD;
Mediterranean areas. This is the subject of the current work, which presents an&#xD;
experiment based on the integration of remotely sensed images and sample field&#xD;
measurements aimed at producing forest attribute maps in central Italy. Testing&#xD;
was carried out in an area where 370 geocoded field plots, sampled on a singlestage&#xD;
cluster design, were collected to characterize wood and non-wood forest&#xD;
attributes. These ground data served to apply various k-Nearest Neighbour (k-&#xD;
NN) estimation procedures to multitemporal Landsat 7 ETM+ images in order&#xD;
to map major forest attributes (basal area and simulated leaf area index, LAI).&#xD;
More specifically, the investigation focused on evaluating the effects of using&#xD;
satellite images from different periods of the growing season and spectral metrics&#xD;
of increasing complexity. The results achieved by the examined methods are&#xD;
finally discussed in order to provide guidelines for possible operational&#xD;
utilization.
Description: L'articolo è disponibile sul sito dell'editore http://www.tandf.co.uk/journals/</summary>
    <dc:date>2004-12-31T23:00:00Z</dc:date>
  </entry>
  <entry>
    <title>K-NN FOREST: a software for the non-parametric prediction and mapping of environmental variables by the k-Nearest Neighbors algorithm</title>
    <link rel="alternate" href="http://hdl.handle.net/2067/2333" />
    <author>
      <name>Chirici, Gherardo</name>
    </author>
    <author>
      <name>Corona, Piermaria</name>
    </author>
    <author>
      <name>Marchetti, Marco</name>
    </author>
    <author>
      <name>Mastronardi, Alessandro</name>
    </author>
    <author>
      <name>Maselli, Fabio</name>
    </author>
    <author>
      <name>Bottai, Lorenzo</name>
    </author>
    <author>
      <name>Travaglini, Davide</name>
    </author>
    <id>http://hdl.handle.net/2067/2333</id>
    <updated>2013-01-11T00:05:09Z</updated>
    <published>2011-12-31T23:00:00Z</published>
    <summary type="text">Title: K-NN FOREST: a software for the non-parametric prediction and mapping of environmental variables by the k-Nearest Neighbors algorithm
Authors: Chirici, Gherardo; Corona, Piermaria; Marchetti, Marco; Mastronardi, Alessandro; Maselli, Fabio; Bottai, Lorenzo; Travaglini, Davide
Abstract: In the last decades researchers investigated the possibility of extending the information collected in sampling units during a field survey to wider geographical areas through the use of remotely sensed images. One of the most widely adopted approaches is based on the non-parametric k-Nearest Neighbors (k-NN) algorithm. This contribution describes the software K-NN FOREST we developed to provide a complete tool for the implementation of the k-NN technique to generate spatially explicit estimations (maps) of a response variable acquired in the field by sampling units through the use of remotely sensed data or other ancillary variables. K-NN FOREST is designed to guide the user through a graphic user interface in the different phases of the process. K-NN FOREST is freely available for download and it is designed to run under Windows environment in conjunction with the GIS software IDRISI.</summary>
    <dc:date>2011-12-31T23:00:00Z</dc:date>
  </entry>
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