UAS 기반, 가시, 근적외 및 열적외 영상을 활용한 식생조사
Vegetation Monitoring using Unmanned Aerial System based Visible, Near Infrared and Thermal Images
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초록
최근 영농분야에서 종자파종, 병충해 방제 등에 무인항공기(UAV ; Unmanned Aerial Vehicle)를 활용한 응용이 활발히 진행되고 있다. 본 연구는 UAV에 다양한 파장대의 영상센서를 탑재하고 SfM (Structure from Motion) 영상해석기법과 연계한‘고해상 저고도 원격탐측시스템(UAS ; Unmanned Aerial System)’를 구성, UAS 기반 식생조사의 효용성을 고찰하여 정밀영농의 활용성을 검토하였다. 이를 위해 저가 UAV에 가시 컬러(VIS_RGB ; Visible Red, Green, and Blue) 영상센서, 수정된 BG_NIR (Blue Green_Near Infrared Red) 근적외 영상 센서, 7.5∼13.5㎛ 분광대역의 열적외 영상(TIR ; Thermal Infrared Red)센서를 조합 연계한 UAS를 구성하였다. 또한, 가시·근적외 및 열적외 파장대를 기본요소로 광합성에 따른 식물의 엽록소, 질소 및 수분 함유량 등을 검토할 수 있는 총 10종의 식생지수를 선정, 식생상태 검출에 활용하였다. 시험대상지에 대한 각 파장대역의 영상을 획득하고 사전에 조사된지상 피복현황을 기준으로 각 식생지수의 분포도 및 식생지수 간 상관성(결정계수 R2) 등을 비교·고찰하여 무인항공기를 활용한 가시 컬러, 근 적외 및 열 적외 영상에 의한 식생상태의 검측 수행능력을 검토하였다. 저가 무인항공기에 VIS_RGB, BG_NIR 및 TIR 영상 센서를 탑재, 식생조사의 효용성을종합적으로 검토한 결과, 인공위성과 항공영상에 의존한 과거의 식생조사방식 대비, 영상해상도, 경제성 및 운용성 면에서 UAV기반 고해상 저고도 원격탐측시스템(UAS)의 효용성을 입증할 수 있었으므로 정밀농업, 수계 및 산림조사 등의 분야에 그 활용이 기대된다.
Abstract
In recent years, application of UAV(Unmanned Aerial Vehicle) to seed sowing and pest control has been actively carried out in the field of agriculture. In this study, UAS(Unmanned Aerial System) is constructed by combining image sensor of various wavelength band and SfM((Structure from Motion) based image analysis technique in UAV. Utilization of UAS based vegetation survey was investigated and the applicability of precision farming was examined. For this purposes, a UAS consisting of a combination of a VIS_RGB(Visible Red, Green, and Blue) image sensor, a modified BG_NIR(Blue Green_Near Infrared Red) image sensor, and a TIR(Thermal Infrared Red) sensor with a wide bandwidth of 7.5μm to 13.5μm was constructed for a low cost UAV. In addition, a total of ten vegetation indices were selected to investigate the chlorophyll, nitrogen and water contents of plants with visible, near infrared, and infrared wavelength's image sensors. The images of each wavelength band for the test area were analyzed and the correlation between the distribution of vegetation index and the vegetation index were compared with status of the previously surveyed vegetation and ground cover. The ability to perform vegetation state detection using images obtained by mounting multiple image sensors on low cost UAV was investigated. As the utility of UAS equipped with VIS_RGB, BG_NIR and TIR image sensors on the low cost UAV has proven to be more economical and efficient than previous vegetation survey methods that depend on satellites and aerial images, is expected to be used in areas such as precision agriculture, water and forest research.
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