Journal of Cadastre & Land InformatiX (지적과 국토정보)

LX Spatial Information Research Institute (한국국토정보공사 공간정보연구원)
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The Analysis of Evergreen Tree Area Using UAV-based Vegetation Index

UAV 기반 식생지수를 활용한 상록수 분포면적 분석

  • Lee, Geun-Sang (Department of Cadastre & Civil Engineering, Vision College of Jeonju)
  • 이근상 (전주비전대학교 지적토목학과)
  • Received : 2017.03.14
  • Accepted : 2017.06.20
  • Published : 2017.06.30
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Abstract

The decrease of green space according to the urbanization has caused many environmental problems as the destruction of habitat, air pollution, heat island effect. With interest growing in natural view recently, proper management of evergreen tree which is lived even the winter season has been on the rise importantly. This study analyzed the distribution area of evergreen tree using vegetation index based on unmanned aerial vehicle (UAV). Firstly, RGB and NIR+RG camera were loaded in fixed-wing UAV and image mosaic was achieved using GCPs based on Pix4d SW. And normalized differences vegetation index (NDVI) and soil adjusted vegetation index (SAVI) was calculated by band math function from acquired ortho mosaic image. validation points were applied to evaluate accuracy of the distribution of evergreen tree for each range value and analysis showed that kappa coefficient marked the highest as 0.822 and 0.816 respectively in "NDVI > 0.5" and "SAVI > 0.7". The area of evergreen tree in "NDVI > 0.5" and "SAVI > 0.7" was $11,824m^2$ and $15,648m^2$ respectively, that was ratio of 4.8% and 6.3% compared to total area. It was judged that UAV could supply the latest and high resolution information to vegetation works as urban environment, air pollution, climate change, and heat island effect.

도시화에 따른 녹지의 감소로 서식처 피괴, 대기오염, 열섬효과 등 많은 환경문제들이 발생하고 있다. 최근에는 자연경관에 대한 관심이 높아지면서 겨울철에도 서식하는 상록수의 적정 관리가 중요하게 대두되고 있다. 본 연구에서는 UAV 기반 식생지수를 이용하여 상록수 분포면적을 분석하였다. 먼저 고정익 UAV에 RGB와 NIR+RG 카메라를 탑재하였으며 Pix4D SW 기반 GCP점을 활용하여 영상접합을 수행하였다. 그리고 취득한 정사영상으로부터 밴드계산 기능을 통해 NDVI와 SAVI 식생지수를 계산하였다. 식생지수 구간별 상록수 분포의 정확도를 평가하기 위해 검정점을 이용하였으며, 분석 결과 "NDVI > 0.5"와 "SAVI > 0.7" 구간에서 Kappa 계수가 각각 0.822와 0.816로 가장 높게 나타났다. GIS 공간분석을 통해 계산한 "NDVI > 0.5"와 "SAVI > 0.7" 구간에서의 상록수 분포면적은 각각 $11,824m^2$와 $15,648m^2$로 계산되었으며 이는 전체면적 대비 4.8%와 6.3%에 해당되는 비율이다. 이와 같이 도심지 환경, 대기오염, 기후변화, 열섬효과 등과 관련하여 식생을 분석하는 업무에서 UAV가 최신의 고해상도 정보를 제공해 줄 수 있으리라 판단된다.

Keywords

References

  1. 김건우, 권치원, 임효진, 염동걸, 최윤호, 정다워, 임현희, 신호상, 박범진. 2013. 겨울철 소나무숲의 NVOC 농도와 숲환경과의 관계. 한국산림휴양학회지. 17(4):133-141. Kim GW, Kwon CW, Lim HJ, Yeom DG, Choi YH, Joung DW, Lim HH, Shin HS, Park BJ. 2013. Realationship between NVOC and Korean Red Pine(Pinus densiflora) Forest Environment in Winter Season. Journal of the Korea Institute of Forest Recreation Welfare. 17(4):133-141.
  2. 김미경, 김상필, 김남훈, 손홍규. 2014. LANDSAT 영상을 이용한 세종특별자치시의 도시화와 열섬현상 분석. 대한토목학회논문집. 34(3):1033-1041. Kim MG, Kim SP, Kim NH, Sohn HG. 2014. Urbanization and Urban Heat Island Analysis Using LANDSAT Imagery: Sejong City As a Case Study. Journal of Korean Society of Civil Engineers. 34(3):1033-1041. https://doi.org/10.12652/Ksce.2014.34.3.1033
  3. 김인환, 한경수, 김상일. 2011. 1KM NDVI 10년자료를 이용한 한반도 식생의 경년변동 분석. 대한원격탐사학회지. 27(1):17-24. Kim IH, Han KS, Kim SI. 2011. Vegetation Interannual variavility Over Korea Using 10-Years 1KM NDVI Data. Korean Journal of Remote Sensing. 27(1):17-24. https://doi.org/10.7780/kjrs.2011.27.1.017
  4. 김영표. 2012. GIS 공간통계기법을 이용한 NDVI 분포특성 분석. 한국산림휴양학회지. 16(3):101-107. Kim YP. 2012. Distribution Characteristics Analysis of NDVI Using Spatial Statistical Method with GIS. Journal of the Korea Institute of Forest Recreation Welfare. 16(3):101-107.
  5. 류재현, 한경수, 피경진, 이미지. 2013. 식생자료를 이용한 동아시아 사막 주변의 토지피복 변화 분석. 대한원격탐사학회지. 29(1):105-114. Ryu JH, Han KS, Pi KJ, Lee MJ. 2013. Analysis of Land Cover Change Around Desert Areas of East Asia. Korean Journal of Remote Sensing. 29(1):105-114. https://doi.org/10.7780/kjrs.2013.29.1.10
  6. 박인환, 조광진, 사공정희, 조현주, 김진효, 이혜영. 2012. 충남 서천군에 분포하는 상록침엽수림의 식생분포와 특성. 한국산림휴양학회지. 16(1):59-68. Park IH, Cho KJ, Sagong JH, Cho HJ, Kim JH, Lee HY. 2012. A Study on Vegetation Distribution and Characteristics of the Evergreen Coniferous Forest in Seochoen-gun, Chungcheongnam-do. Journal of the Korean Institute of Forest Recreation Welfare. 16(1):59-68.
  7. 박정기, 고신영, 조기성. 2013. KOMPSAT-2 영상 및 고해상도 항공영상을 이용한 도심지역 식생분류. 한국지형공간정보학회지. 21(4):21-27. Park JG, Go SY, Cho GS. 2013. Vegetation Classification using KOMPSAT-2 Imagery and High-resolution airborne imagery in Urban Area. Journal of Korean Society for Geospatial Information Science. 21(4):21-27. https://doi.org/10.7319/kogsis.2013.21.4.021
  8. 에르뎅솜베 술드, 조기성. 2016. Landsat 위성영상을 이용한 몽골 Tuul-Basin 지역의 토지피복변화 및 지표온도 시계열적 분석. 한국지형공간정보학회지. 24(3):39-47. Erdenesumbee Suld, Cho GS. 2016. Time series Analysis of Land Cover Change and Surface Temperature in Tuul-Basin, Mongolia Using Landsat Satellite Image. Journal of the Korean Society for Geospatial Information Science. 24(3):39-47. https://doi.org/10.7319/KOGSIS.2016.24.3.039
  9. 염종민, 한경수, 이창석, 박윤영, 김영섭. 2008. SPOT/VEGETATION NDVI 자료를 이용한 북한지역. 한국지리정보학회지. 11(2):28-37. Yeom JM, Han KS, Lee CS, Park YY, Kim YS. 2008. A Detection of Vegetation Variation Over North Korea using SPOT/VEGETATION NDVI. Journal of the Korean Association of Geographic Information Studies. 11(2):28-37.
  10. 장명준, 신예철, 최형선, 김태호. 2012. 도시규모를 고려한 탄소배출량과 도시특성요소와의 관계 연구. 한국도시행정학회 도시행정학보. 25(4):57-87. Jang MJ, Shin YC, Choi HS, Kim TH. 2012. The Analysis of Relationship Between Urban Size and Co2 Emissions Considering Urban Characteristics. Journal of the Korean Urban Management Association. 25(4):57-87.
  11. 최석근, 이승기, Wang Baio. 2014. Landsat 8 OLI영상의 NDVI를 이용한 식생피복지수 분석. 한국측량학회지. 32(1):9-17. Choi SK, Lee SK, Wang Baio. 2014. Analysis of Vegetation Cover Fraction on Landsat OLI using NDVI. Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography. 32(1):9-17. https://doi.org/10.7848/ksgpc.2014.32.1.9
  12. Gilabert MA, Gonzalez-Piqueras J, Garcia-Haro FJ, Melia J. 2002. A generalized soil-adjusted vegetation index. Remote Sensing of Environment. 82:303-310. https://doi.org/10.1016/S0034-4257(02)00048-2
  13. Huete AR. 1988. A Soil-adjusted vegetation index(SAVI). Remote sensing of Environment. 25:295-309. https://doi.org/10.1016/0034-4257(88)90106-X
  14. Jesper Rasmussen, Georgios Ntakos, Jon Nielsen, Jesper Svensgaard, Robert N. Poulsen, Svend Christensen. 2016. Are vegetation indices derived from consumer-grade cameras mounted on UAVs sufficiently reliable for assessing experimental plots?. European Journal of Agronomy. 74:75-92. https://doi.org/10.1016/j.eja.2015.11.026
  15. Juliane Bendig, Kang Yu, Helge Aasen, Andreas Bolten, Simon Bennertz, Janis Broscheit, Martin L. Gnyp, Georg Bareth. 2015. Combining UAV-based plant height from crop surface models, visible, and near infrared vegetation indices for biomass monitoring in barley. International Journal of Applied Earth Observation and Geoinformation. 39:79-87. https://doi.org/10.1016/j.jag.2015.02.012
  16. Lee KD, Na SI, Baek SC, Jung BJ, Hong SY. 2015. The Study of Applicability to Fixed-field Sensor for Normalized Difference Vegetation Index (NDVI) Monitoring in Cultivation Area. Korean Journal of Soil Science and Fertilizer. 48(6):593-601. https://doi.org/10.7745/KJSSF.2015.48.6.593
  17. Na SI, Hong SY, Park CW, Kim KD, Lee KD. 2016. Estimation of Highland Kimchi Cabbage Growth using UAV NDVI and Agro-meteorological Factors. Korean Journal of Soil Science and Fertilizer. 49(5):420-428. https://doi.org/10.7745/KJSSF.2016.49.5.420
  18. Nagai M, Chen T, Ahmed A, Shibasaki R. 2008. UAV Borne Mapping by Multi Sensor Integration. ISPRS XXI Congress. 1215-1221.
  19. Torres-Sanchez J, Lopez-Granados F, Pena JM. 2015. An automatic object-based method for optimal thresholding in UAV images: Application for vegetation detection in herbaceous crops. Computers and Electronics in Agriculture. 114:43-52. https://doi.org/10.1016/j.compag.2015.03.019
  20. Villa P. 2012. Mapping urban growth using soil and vegetation index and landat data: The Milan (Italy) City Area Case Study. Landscape and Urban Planning. 107(3):245-254. https://doi.org/10.1016/j.landurbplan.2012.06.014
  21. Yutaka Watanabe, Yoshihisa Kawahara. 2016. UAV Photogrammetry for monitoring changes in river topography and vegetation. Procedia Engineering. 154:317-325. https://doi.org/10.1016/j.proeng.2016.07.482

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