Journal of the Korean Association of Geographic Information Studies (한국지리정보학회지)

The Korean Association of Geographic Information Studies (한국지리정보학회)
권호 표지
DOI QR코드

DOI QR Code

Study on the Estimation of leaf area index (LAI) of using UAV vegetation index and Tree Height data

UAV 식생지수 및 수고 자료를 이용한 엽면적지수(LAI) 추정 연구

  • MOON, Ho-Gyeong (Division of Convergence Research, Bureau of Ecological Research, National Institute of Ecology) ;
  • CHOI, Tae-Young (Division of Convergence Research, Bureau of Ecological Research, National Institute of Ecology) ;
  • KANG, Da-In (Division of Convergence Research, Bureau of Ecological Research, National Institute of Ecology) ;
  • CHA, Jae-Gyu (Division of Convergence Research, Bureau of Ecological Research, National Institute of Ecology)
  • Received : 2018.12.03
  • Accepted : 2018.12.20
  • Published : 2018.12.31
Download PDF
⟨ Previous Next ⟩

Abstract

The leaf area index (LAI) is a major factor explaining the photosynthesis of vegetation, evapotranspiration, and energy exchange between the earth surface and atmosphere, and there have been studies on accurate and applicable LAI estimation methods. This study aimed to investigate the relationship between the actual LAI data, UAV image-based vegetation index, canopy height and satellite image (Sentinel-2) LAI and to present an effective LAI estimation method using UAV. As a result, among the six vegetation indices in this study, NDRE ($R^2=0.496$) and CIRE ($R^2=0.443$), which contained red-edge band, showed a high correlation. The application of the canopy height model data to the vegetation index improved the explanatory power of the LAI. In addition, in the case of NDVI, the saturation problem caused by the linear relationship with LAI was addressed. In this study, it was possible to estimate high resolution LAI using UAV images. It is expected that the applicability of such data will be improved if calibration and correction steps are carried out for various vegetation and seasonal images.

엽면적지수(LAI: Leaf Area Index)는 식생의 광합성, 증발산, 지표면과 대기사이의 에너지 교환 등을 설명하는 주요 인자로서, 정확하고 활용성 높은 LAI 추정 기법에 대한 연구들이 진행되었다. 본 연구에서는 UAV를 이용한 LAI 추정 방법을 모색하기 위하여 현장 실측된 LAI 자료와 UAV 영상기반의 식생지수, 수고 및 위성영상(Sentinel-2) LAI 간의 관계성을 파악하고 효과적인 UAV LAI 산정방법을 제시하고자 하였다. 그 결과 연구에 활용된 6종의 식생지수 중 Red-edge band를 포함하고 있는 NDRE ($R^2=0.496$), CIRE ($R^2=0.443$)가 LAI 추정에 효과적인 식생지수로 나타났다. 수고(Canopy Height Model) 자료를 식생지수에 적용하였을 때 LAI에 대한 설명력이 향상되었으며, NDVI의 경우에 LAI와의 선형관계에서 발생되는 포화문제(saturation problem)를 보였던 구간(0.85)이 일부 해소됨을 확인하였다.

Keywords

References

  1. Bang D.S., D.G. Lee, S.R. Yang and H.J. Lee. 2018. Study on the Tree Height Using Unmanned Aerial Photogrammetry Method. Journal of the Korean Association of Geographic Information Studies 21(3):35-47. https://doi.org/10.11108/kagis.2018.21.3.035
  2. Barnes, E.M., T.R. Clarke and S.E. Richards. 2000. Coincident detection of crop water stress, nitrogen status and canopy density using ground based multispectral data. Proceedings of the Fifth International Conference on Precision Agriculture.
  3. Chason, J.W., D.D. Baldocchi and M.A. Huston. 1991. A comparison of direct and indirect methods for estimating forest canopy leaf area. Agricultural and Forest Meteorology 57(1):107-128. https://doi.org/10.1016/0168-1923(91)90081-Z
  4. Chen, J.M. 1996. Optically-based methods for measuring seasonal variation of leaf area index in boreal conifer stands. Agricultural and Forest Meteorology 80(2):135-163. https://doi.org/10.1016/0168-1923(95)02291-0
  5. Chen, J.M., P.M. Rich, T.S. Gower, J.M. Norman and S. Plummer. 1997. Leaf area index of boreal forests: theory, techniques and measurements. Journal of Geophysical Research 102(29):429-444.
  6. Delegido, J., J. Verrelst, C.M. Meza, J.P. River, L. Alonso and J. Moreno. 2011. Evaluation of Sentinel-2 Red-Edge Bands for Empirical Estimation of Green LAI and Chlorophyll Content. Journal of Sensors 11:7063-7081. https://doi.org/10.3390/s110707063
  7. Delegido, J., J. Verrelst, C.M. Meza, J.P. River, L. Alonso and J. Moreno. 2013. A red-edge spectral index for remote sensing estimation of green LAI over agroecosystems. Eur. J. Agron 46:45-52.
  8. Decagon Devices Inc. 2017. AccuPAR PAR /LAI Ceptometer(Model LP-80) Operator's Manual. ver. July 10.
  9. Fan, X., K. Kawamura, T.D. Xuan, N. Yuba and J. Lim. 2017. Low‐cost visible and near‐infrared camera on an unmanned aerial vehicle for assessing the herbage biomass and leaf area index in an Italian ryegrass field, Grassland Science 64: 145-150.
  10. Gitelson, A.A. 2004. Wide dynamic range vegetation index for remote quantification of biophysical characteristics of vegetation. Journal of Plant Physiology 161:165-173. https://doi.org/10.1078/0176-1617-01176
  11. Gitelson, A.A., M. Mark and L. Hatrmut. 1996. Detection of Red Edge Position and Chlorophyll Content by Reflectance Measurements Near 700nm. Journal of Plant Physiology Vol(148):501-508.
  12. Herwitz, S.R., L.F. John, S.E. Dunagan, R.G. Higgins, D.V. Sullivan, J. Zheng, B.M. Lobitz, J.G. Leung, B.A. Gallmeyer, M. Aoyagi, R.E. Slye and J.A. Brass. 2004. Imaging from an unmanned aerial vehicle: agricultural surveillance and decision support, Computers and Electronics in Agriculture 44: 49-61. https://doi.org/10.1016/j.compag.2004.02.006
  13. Herrmann, I., A. Pimstein, A. Karnieli, Y. Cohen, V. Alchanatis and D. Bonfil. 2011. LAI assessment of wheat and potato crops by VEN${\mu}$S and Sentinel-2 bands, Remote Sensing of Environment 115(8): 2141-2151. https://doi.org/10.1016/j.rse.2011.04.018
  14. Jordan, C.F. 1969. Derivation of leaf area index from quality of light on the forest floor, Ecology 50: 663-666. https://doi.org/10.2307/1936256
  15. Kwon, B.R., J.H. Jeon, H.S. Kim and M.J. Lee. 2016. Estimation of Specific Leaf Area Index Using Direct Method by Leaf Litter in Gwangneung, Mt. Taewha and Mt. Gariwang. Korean Journal of Agricultural and Forest Meteorology 18(1):1-15. https://doi.org/10.5532/KJAFM.2016.18.1.1
  16. Kalisperakis, I., Ch. Stentoumisa, L. Grammatikopoulosb, K. Karantzalosc. 2015. Leaf area index estimation in vineyards from UAV hyperspectral data, 2D image mosaics and 3D canopy surface models. Remote Sensing and Spatial Information Sciences Vol. XL-1/W4.
  17. Kim, T.G. 2008. Integration of ASTER and MODIS data to generate LAI map of South Korea. Doctoral Thesis, In-Ha Univ., South Korea. pp.8-9.
  18. Lee, K.S., S.H. Kim, Y.L. Park and K.C. Jang. 2003. Generation of Leaf Area Index(LAI) Map Using Multispectral Satellite Data and Field Measurements, Korean Journal of Remoote Sensing, 19(5):371-380.
  19. Lee, J.M. 2017. Monthly LANDSAT LAI Generation in Korean Peninsula. Master Thesis, Kun-Kuk Univ. South Korea. goo.
  20. Lee, K.D., C.W. Park, K.H. So and S.I. Na. 2017. Estimating of Transplanting Period of Highland Kimchi Cabbage Using UAV Imagery. Journal of Korean Society of Soil Science and Fertilizer 59(6):39-50.
  21. Lee, K., S. Kim, J. Park, T. Kim, Y. Park and C. Woo. 2006. Estimation of forest LAI in close canopy situation using optical remote sensing data, Korean Journal of Remote Sensing 22(5): 305-311.
  22. Lim, O.T. 2016. A study on identifying individual Korean fir (Abies koreana) trees at Mt. Halla, Jeju, Korea, using UAV (Unmanned Aerial Vehicle) images and the Object-based Image Analysis method UAV (Unmanned Aerial Vehicle). Master's thesis, Kookmin University. Korea.
  23. Lim, Y.S., Y.D. Eo, M.C. Jeon, M.H. Lee and P.M. Wook. 2016. Experiments of Individual Tree and Crown Width Extraction by Band Combination Using Monthly Drone Images. The Korea Society For GeospatIal Information System 24(4):67-74.
  24. Lee, Y.C. 2018. Vegetation Monitoring using Unmanned Aerial System based Visible, Near Infrared and Thermal Images. Journal of cadastre & land informatiX 48(1):79-91.
  25. Lee, G.S., Y.W. Woong, J.K. Jung and G.S. Cho. 2015. Analysis of the Spatial Information Accuracy According to Photographing Direction of Fixed Wing. Journal of the Korean Association of Geographic Information Studies 17(3):141-149.
  26. Mathews, A.J. and J.L.R. Jensen. 2013. Visualizing and Quantifying Vineyard Canopy LAI Using an Unmanned Aerial Vehicle (UAV) Collected High Density Structure from Motion Point Cloud. Remote Sensing 5(5):2164-2183. https://doi.org/10.3390/rs5052164
  27. Milas, A.S., M. Romanko, P. Reil, T. Abeysinghe and A. Marambe. 2018. The importance of leaf area index in mapping chlorophyll content of corn under different agricultural treatments using UAV images. International Journal of Remote Sensing 39:5415-5431. https://doi.org/10.1080/01431161.2018.1455244
  28. Moon, H.G., S.M. Lee and J.G. Cha. 2017. Land Cover Classification Using UAV Imagery and Object-Based Image Analysis -Focusing on the Maseo-myeon, Seocheon -gun, Chungcheongnam-do-. Journal of the Korean Association of Geographic Information Studies 20(1):1-14. https://doi.org/10.11108/KAGIS.2017.20.1.001
  29. Na, S.I., S.Y. Hong, C.W. Park, K.D. Kim and K.D. Lee. 2016. Estimation of Highland Kimchi Cabbage Growth using UAV NDVI and Agro-meteorological Factors, Journal of Korean Society of Soil Science and Fertilizer 49(5): 420-428 (in Korean). https://doi.org/10.7745/KJSSF.2016.49.5.420
  30. NA, S.I., C.W. Park, Y.K. Cheong, C.S. Kang, I.B. Choi and K.D. Lee. 2016. Selection of Optimal Vegetation Indices for Estimation of Barley & Wheat Growth based on Remote Sensing - An Application of Unmanned Aerial Vehicle and Field Investigation Data - Korea Journal of Remote Sensing 32(5):483- 497. https://doi.org/10.7780/kjrs.2016.32.5.7
  31. Park, H.K., W.Y. Choi, N.H. Back, S.S. Kim and K.K. Kim. 2004. Estimation of Leaf Area Index by Plant Canopy Analyzer in Rice. Korean journal of crop science 48(6): 463-467.
  32. Perko, R., R. Hannes, D. Janik eutscher, G. Karlheinz and S. Mathias. 2011. Forest Assessment Using High Resolution SAR Data in X-Band Data in X-Band. Remote Sens(3):792-815.
  33. Park, J.K. and J.H. Park. 2015. Crop classification using imagery of unmanned aerial vehicle. Journal of the Korean Society of Agricultural Engineers 57(6): 91-9. https://doi.org/10.5389/KSAE.2015.57.6.091
  34. Pearson, R.L. and L.D. Miller. 1972. Remote mapping of standing crop biomass for estimation of the productivity of the shortgrass prairie. Proc. of the Eighth International Symposium on Remote Sensing of Environment II, Michigan, Ann Arbor 1357-1381.
  35. Pu, R., P. Gong, G.S. Biging and M.R. Larrieu. 2003. Extraction of red edge optical parameters from Hyperion data for estimation of forest leaf area index, IEEE Transactions on Geoscience and Remote Sensing 41(4): 916-921. https://doi.org/10.1109/TGRS.2003.813555
  36. Pix4d. 2018. https://support.pix4d.com/hc/en-us.
  37. Rouse, J.W., R.H. Haas, J.A. Schell and D.W. Deering. 1973. Monitoring vegetation systems in the Great Plains with ERTS. Third ERTS Symposium Vol(1):309-317.
  38. Stenberg. P., S. Linder, H. Smolander and J.f. Ellis. 1994. Performance of the LAI-2000 plant canopy analyzer in estimation leaf area index of some Scots pine stands. Tree Physiology 14:981- 995. https://doi.org/10.1093/treephys/14.7-8-9.981
  39. Turner, D.P., W.B. Cohen, R.E. Kennedy, K.S. Fassnacht and J.M. Briggs. 1999. Relationships between leaf area index and Landsat TM spectral vegetation indices across three temperate zone sites, Remote Sensing of Environment 70(1):52-68. https://doi.org/10.1016/S0034-4257(99)00057-7
  40. Verrelst. J., J.P. Rivera, F. Veroustraete, J. Munoz-Mari, Jan G.P.W. Clevers, G.C. Valls and J. Moreno. 2015. Experimental Sentinel-2 LAI estimation using parametric, non-parametric and physical retrieval methods - A comparison. Journal of Photogrammetry and Remote Sensing 108:260-272. https://doi.org/10.1016/j.isprsjprs.2015.04.013
  41. Vincini, M., E. Frazzi and P. DAlessio. 2008. A broad-band leaf chlorophyll index at the canopy scale. Precision Agriculture 9:303-319. https://doi.org/10.1007/s11119-008-9075-z
  42. Wu, Chaoyang., Z. Niu, Q. Tang, W. Huang, B. Rivard and F. Benoit. 2009. Remote estimation of gross primary production in wheat using chlorophyll-related vegetation indices. Agricultural and Forest Meteorology 149:1015-1021. https://doi.org/10.1016/j.agrformet.2008.12.007

Cited by

  1. 광학위성영상을 이용한 기계학습/PROSAIL 모델 기반 엽면적지수 추정 vol.37, pp.6, 2018, https://doi.org/10.7780/kjrs.2021.37.6.1.19