본문 바로가기
  • Home

Comparative Evaluation of UAV NIR Imagery versus in-situ Point Photo in Surveying Urban Tributary Vegetation

  • Journal of Environmental Impact Assessment
  • Abbr : J EIA
  • 2018, 27(5), pp.475-488
  • Publisher : Korean Society Of Environmental Impact Assessment
  • Research Area : Engineering > Environmental Engineering

이정주 1 황영석 1 Seong-Il Park 1 Um, Jung Sup 1

1경북대학교

Accredited

ABSTRACT

Surveying urban tributary vegetation is based mainly on field sampling at present. The tributary vegetation survey integrating UAV NIR (Unmanned Aerial Vehicle Near Infrared Radiance) imagery and in-situ point photo has received only limited attentions from the field ecologist. The reason for this could be the largely undemonstrated applicability of UAV NIR imagery by the field ecologist as a monitoring tool for urban tributary vegetation. The principal advantage of UAV NIR imagery as a remote sensor is to provide, in a cost-effective manner, information required for a very narrow swath target such as urban tributary (10m width or so), utilizing very low altitude flight, realtime geo-referencing and stereo imaging. An exhaustive and realistic comparison of the two techniques was conducted, based on operational customer requirement of urban tributary vegetation survey: synoptic information, ground detail and quantitative data collection. UAV NIR imagery made it possible to identify area-wide patterns of the major plant communities subject to many different influences (e.g. artificial land use pattern), which cannot be acquired by traditional field sampling. Although field survey has already gained worldwide recognition by plant ecologists as a typical method of urban tributary vegetation monitoring, this approach did not provide a level of information that is either scientifically reliable or economically feasible in terms of urban tributary vegetation (e.g. remedial field works). It is anticipated that this research output could be used as a valuable reference for area-wide information obtained by UAV NIR imagery in urban tributary vegetation survey.

Citation status

* References for papers published after 2022 are currently being built.