TY  - CONF
  - O'Sullivan, B, Keady, S, Keane, E, Irwin, S, O'Halloran, J
  - 2010 conference on ECAI 2010: 19th European Conference on Artificial Intelligence
  - Data mining for biodiversity prediction in forests
  - 2010
  - Unknown
  - Validated
  - 0
  - Scopus: 1 ()
  - 289
  - 294
  - There is international consensus on the key elements of sustainable forest management. Biological diversity has been recognised as one of them. This paper investigates the usefulness of terrestrial laser scanning technology in forest biodiversity assessment. Laser scanning is a rapidly emerging technology that captures high-resolution, 3-D structural information about forests and presently has applications in standing timber measurement. Forest biodiversity is influenced by structural complexity in the forest although precise repeatable measures are difficult to achieve using traditional methods. The aim of the research presented here is to apply laser scanning technology to the assessment of forest structure and deadwood, and relate this information to the diversity of plants, invertebrates and birds in a range of forest types including native woodlands and commercial plantations. Procedures for forest biodiversity assessment are known to be expensive due to their reliance on labour-intensive field visits. We describe our progress on the application of terrestrial laser scanning in an automated approach to biodiversity assessment. We apply regression techniques from the field of data mining to predict several biodiversity measures using physical attributes of the forest with very promising results. © 2010 The authors and IOS Press. All rights reserved.
  - http://www.scopus.com/inward/record.url?eid=2-s2.0-77956036268;partnerID=40;md5=f193fd0c4f2958252404237511aab3c3
  - 10.3233/978-1-60750-606-5-289
DA  - 2010/NaN
ER  - 

@inproceedings{V66428171,
   = {O'Sullivan,  B and  Keady,  S and  Keane,  E and  Irwin,  S and  O'Halloran,  J },
   = {2010 conference on ECAI 2010: 19th European Conference on Artificial Intelligence},
   = {{Data mining for biodiversity prediction in forests}},
   = {2010},
   = {Unknown},
   = {Validated},
   = {0},
   = {Scopus: 1 ()},
  pages = {289--294},
   = {{There is international consensus on the key elements of sustainable forest management. Biological diversity has been recognised as one of them. This paper investigates the usefulness of terrestrial laser scanning technology in forest biodiversity assessment. Laser scanning is a rapidly emerging technology that captures high-resolution, 3-D structural information about forests and presently has applications in standing timber measurement. Forest biodiversity is influenced by structural complexity in the forest although precise repeatable measures are difficult to achieve using traditional methods. The aim of the research presented here is to apply laser scanning technology to the assessment of forest structure and deadwood, and relate this information to the diversity of plants, invertebrates and birds in a range of forest types including native woodlands and commercial plantations. Procedures for forest biodiversity assessment are known to be expensive due to their reliance on labour-intensive field visits. We describe our progress on the application of terrestrial laser scanning in an automated approach to biodiversity assessment. We apply regression techniques from the field of data mining to predict several biodiversity measures using physical attributes of the forest with very promising results. © 2010 The authors and IOS Press. All rights reserved.}},
   = {http://www.scopus.com/inward/record.url?eid=2-s2.0-77956036268;partnerID=40;md5=f193fd0c4f2958252404237511aab3c3},
   = {10.3233/978-1-60750-606-5-289},
  source = {IRIS}
}