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http://dx.doi.org/10.11108/kagis.2018.21.4.158

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

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)
Publication Information
Journal of the Korean Association of Geographic Information Studies / v.21, no.4, 2018 , pp. 158-174 More about this Journal
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.
Keywords
Unmanned Aerial Vehicle; Red-edge; NDRE (Normalized Difference Red Edge Index); GCP; Sentinel-2A;
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Times Cited By KSCI : 8  (Citation Analysis)
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