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- W8992155 abstract "The scientific community is witnessing a significant increase in the availability of different global satellite derived biophysical data sets. However, the use of such data is currently not supported by accurate in-situ biophysical measurement (e.g. canopy structure) in both a research and operational context for the monitoring of forest and land dynamics. Consequently, there is an urgent need for methods to measure in-situ canopy structure accurately and better integrate with improved and innovative remote sensing approaches. This paper explores the use of a ground-based, upward looking LiDAR instrument, combined with a fully automated analysis method to retrieve the gap fraction distribution. Traditional inventory methods for the assessment of forest structure are less objective or based on a 2D approach. We compare the seasonal dynamics of gap fraction distribution from hemispherical photographs and terrestrial LiDAR measurement during bud break. Preliminary analysis shows that gap fraction distributions derived from terrestrial LiDAR were consistently lower than the values obtained from hemispherical photography. This might indicate that the LiDAR scans at the centre position of the plot are not representing the plot scale variation. However, the LiDAR based methodology is fully automated, requires no operator interference and is more objective, whereas the analysis of hemispherical photographs requires a large number of operator decisions (e.g. thresholding). Further improvements of this LiDAR-based method can still be achieved by (i) a better understanding of scanner settings and data resolution on the derived gap fraction and (ii) integration of target intensity in the analysis. This paper highlighted the high potential and need for a robust method to derive gap fraction distributions to monitor seasonal dynamics in forests." @default.
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- W8992155 date "2011-01-01" @default.
- W8992155 modified "2023-09-27" @default.
- W8992155 title "Applying terrestrial LiDAR to derive gap fraction distribution time series during bud break." @default.
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