Abstract
Multi-spectral (ms) airborne light detection and ranging (lidar) data are increasingly used for mapping purposes. Geometric data are enriched by intensity digital numbers (DNs) and, by utilizing this additional information either directly, or in the form of active spectral vegetation indices (SVIs), enhancements in land cover classification and change monitoring are possible. In the case of SVIs, the indices should be calculated from reflectance values derived from intensity DNs after rigorous calibration. In practice, such calibration is often not possible, and SVIs calculated from intensity DNs are used. However, the consistency of such active ms lidar products is poorly understood. In this study, the authors reported on an ms lidar mission at three different altitudes above ground to investigate SVI consistency. The stability of two families of indices—spectral ratios and normalized differences—was compared. The need for atmospheric correction in case of considerable range difference was established. It was demonstrated that by selecting single returns (provided sufficient point density), it was possible to derive stable SVI products. Finally, a criterion was proposed for comparing different lidar acquisitions over vegetated areas.
Highlights
Light detection and ranging established itself as a unique high-resolution remote sensing technology due to its 3D sampling of land cover and terrain, and its ability to penetrate and characterize vegetation structures from treetop to ground [1]
If the optical path at different wavelength channels is similar, like in the sensors described by Hakala et al [12] and Morsdorf et al [11], or close to each other, like in the Titan ms lidar, it can be assumed that the influence of these factors is reduced or potentially canceled in active spectral vegetation indices (SVIs)
It has been shown that atmospheric correction is needed to compare intensity digital numbers (DNs) values at different ranges and development of the atmospheric correction model is a logical step
Summary
Light detection and ranging (lidar) established itself as a unique high-resolution remote sensing technology due to its 3D sampling of land cover and terrain, and its ability to penetrate and characterize vegetation structures from treetop to ground [1]. Numerous spectral vegetation indices (SVIs) have been developed based on reflectance values derived from image-based DNs for environmental monitoring and change detection [2,3,4]. Modern multi-spectral lidar technology allows for active narrow-band vertical spectral sampling of vegetation profiles and provides an alternative method of deriving SVI maps, active SVIs, which present a new tool for high resolution thematic mapping, enhanced classification and change detection, and forest resource monitoring [5]. The main limitation of spectral vegetation indices derived from passive remote sensing imagery is the dependence on the sun as a source of illumination [6], which leads to sensitivity of passive SVIs to sun position [7] and cloudiness [8]. To maximize the utility and comparability of active SVIs, it is necessary to study their consistency through different sensors and/or different survey configurations
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