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  • 标题:Automatic Segmentation and Characterisation of Forest Stand Parameters using Airborne LIDAR data, Multispectral and Fogis Data
  • 本地全文:下载
  • 作者:O. Diedershagen ; B. Koch ; H. Weinacker
  • 期刊名称:ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
  • 印刷版ISSN:2194-9042
  • 电子版ISSN:2194-9050
  • 出版年度:2004
  • 卷号:XXXVI-8/W2
  • 出版社:Copernicus Publications
  • 摘要:Inventories and monitoring systems are important tools for sustainable forest management and for the protection of nature in general. In order to make appropriate management plans, it is important to obtain information about the actual state of the environment and the extent of possible changes. Up until now, forest inventories have been based on terrestrial surveys that often integrate material from aerial photographs. These terrestrial surveys, however, are time consuming and thus very expensive. This also makes the repetition of these inventories at regular intervals very difficult. It is therefore necessary to automate these methods and to analyse the use of other remote sensing datasets. One possible method for automatic object recognition might be the use of airborne laser scanner data, which permits the extraction of 3-D information. Several publications have shown that inventory parameters can be collected on a single tree or complete stand basis from lidar data (N. SSET , E. 1997; N. SSET ET ALL 2001; N. SSET ET ALL 2003). This paper deals with the automatic segmentation and characterisation of forest stand units. The procedure presented uses 3-D information from a combined laser-line scanner system. Forest stands are automatically delineated using lidar datasets, and the mean tree height and crown cover density is then calculated for each stand unit. Furthermore, the multi-spectral data can be used to extract information on the tree species and the mixture distribution. This paper presents an effective method of automatically detecting stand boundaries
  • 关键词:Lidar; laser; forest inventory; FOGIS; stand delineation; tree species; multi-spectral data; forest parameters
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