Journal of The Remote Sensing Society of Japan
Online ISSN : 1883-1184
Print ISSN : 0289-7911
ISSN-L : 0289-7911
Special issue: Journal of The Remote Sensing Society of Japan
Volume 41 , Issue Supplement
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Translated Paper
  • Yasunori SAITO, Kenji OMASA
    2021 Volume 41 Issue Supplement Pages S1-S10
    Published: 2021
    Released: February 16, 2021

    We have developed two types of laser-induced fluorescence lidar, which are LIFS (laser-induced fluorescence spectrum) lidar and LIFL (laser-induced fluorescence lifetime) lidar. The LIFS lidar consisted of a pulsed 355 nm laser for excitation of plant pigments, a spectrometer covered the entire range of plant fluorescence from 300 nm to 800 nm and a gated intensified CCD array for detection. The synchronous detection was applied to detect the weak fluorescence even in daytime. The LIFL lidar was for measurement of the fast lifetime of chlorophyll a fluorescence in the order of nano-second (ns) and bellow. A 40 pico-second (ps) 532 nm laser was prepared for short time excitation. A detection system was designed to accommodate for the fast lifetime measurement using microchannel plate photomultiplier tube (MCP-PMT) and other fast-rise time equipment. Rise time of the LIFL lidar was estimated to be 196 ps. For data analysis, convolution integration technique was applied to the lidar observation data. Their performance tests were done for trees naturally grown outside. The LIFS lidar observation of zelkova tree leaves 20 m away from the lidar showed change of the fluorescence spectra depending on the growth/senescence period which turned to growth information about production, accumulation, and disorganization of pigments occurring inside the leaves. The LIFL lidar observation showed that the lifetime of chlorophyll a fluorescence of plane tree leaves 30 m away from the lidar at sun-shined position decreased gradually from the morning to noon and had the lowest at 13:00, and increased in the evening. Variation of photosynthetic activity during a day could be monitored. These results will offer a new technique and novel data for remote sensing of vegetation.

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