Vegetation canopy optical and structural variability based on radiometric and laser analysis

Jules R. Dim; Koji Kajiwara; Yoshiaki Honda

doi:10.1117/12.738014

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9 October 2007 Vegetation canopy optical and structural variability based on radiometric and laser analysis

Jules R. Dim, Koji Kajiwara, Yoshiaki Honda

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Proceedings Volume 6742, Remote Sensing for Agriculture, Ecosystems, and Hydrology IX; 67420C (2007) https://doi.org/10.1117/12.738014
Event: SPIE Remote Sensing, 2007, Florence, Italy

Abstract

For a comprehensive vegetation monitoring and/or management, a good understanding of the distribution of the solar radiation energy among components of this vegetation is needed. The energy received by the vegetation is measured by spectroradiometers either at satellite elevations or near the ground (in situ measurements). In this study, in situ, radiometric data and laser scanning techniques are combined, in order to evaluate the contribution of the vegetation structure to the variability of canopy reflectance. Advanced processing laser techniques are not only an efficient tool for the generation of physical models but also give information about the vertical structure of canopies (height, shape, density) and their horizontal extension. To conduct this study, airborne multispectral radiation data and, laser pulse returns are recorded from a low flying helicopter above the vegetation of a boreal forest. These measurements are used to derive canopy optical and structural variables. The impact of the canopy 2-dimensional structural variability on the distribution of the solar radiation reflected by plants of this area is discussed. The results obtained show that the laser technology can be used for the selection of the most appropriate configuration of radiation measurements, and optimization of canopy physical characteristics, in future airborne missions.

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Jules R. Dim, Koji Kajiwara, and Yoshiaki Honda "Vegetation canopy optical and structural variability based on radiometric and laser analysis", Proc. SPIE 6742, Remote Sensing for Agriculture, Ecosystems, and Hydrology IX, 67420C (9 October 2007); https://doi.org/10.1117/12.738014

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