Abstract
The range between a sensor and the target, the incidence angle, and the target reflectance, are known factors that can influence the intensity values of LiDAR data and consequently, its use in many applications. However, very few studies have provided a quantitative analysis of the effects of normalisation of these three factors on forest leaf area index (LAI) estimations. In this paper, using two coniferous tree species (i.e., Scotch pine and Larch pine) as a case study, the effects of intensity normalisation on coniferous forest LAI estimations have, for the first time, been systematically examined and quantified. It was found that the intensity normalisation had a generally positive effect on the improvement of coniferous forest LAI estimations. However, the improvements were very minor. Specifically, the range normalisation did not improve the accuracy of the LAI estimation for either of the two coniferous tree species. The incidence angle and reflectance normalisation improved the accuracy of the LAI estimation for Scotch pine forests; however, they had no effect on the improvement of the LAI estimation for Larch pine forests. This experimental study suggests that range normalisation is not required for forest LAI estimations in areas with small elevation differences (i.e., less than 114 m). The incidence angle and target reflectance normalisation can marginally improve the accuracy of coniferous forest LAI estimations. However, the extent of this improvement varies among species, depending on the choice of incidence angle and reflectance coefficient. Overall, the effects of normalisation of airborne LiDAR intensity on coniferous forest LAI estimations are closely related to topographic conditions (i.e., elevation and slope), the tree species composition, and its associated structural attributes. Therefore, further research should explore the effects of LiDAR intensity normalisation on forest LAI estimations in regions with large elevation differences and diverse forest structures.
Highlights
Light Detection and Ranging (LiDAR) is an active optical remote-sensing technique that uses laser light to densely sample the Earth’s surface
Without range normalisation, the Scotch pine forest was estimated based on the raw intensity data (i.e., laser penetration index (LPI)) and range-normalised intensity data (i.e., LPIR), is presented with a R2 4
Previous studies suggest that variation in in intensity data, caused byby thethe range differences intensity caused range between the sensor and suggest target, isthat thevariation most important factor, whichdata, should be accounted for differences by range between the sensor and target, is the most important factor, which should be accounted for byby range between the sensor and target, is the most important factor, which should be accounted for normalisation in various applications [21,56]
Summary
Light Detection and Ranging (LiDAR) is an active optical remote-sensing technique that uses laser light to densely sample the Earth’s surface. In addition to precise 3D coordinates, most LiDAR systems record “intensity”. The intensity value recorded by a LiDAR sensor, known as the reflection intensity, is equivalent to the amount of energy being emitted from a target. Most commercial LiDAR sensors have a wavelength in the near-infrared (NIR) spectral range. The LiDAR intensity data have been found to be directly related to the spectral reflectance of the reflected energy from objects in Remote Sens. As green vegetation strongly reflects energy in the NIR wavelength, LiDAR intensity data have been widely used for the mapping and monitoring of forest ecosystems [2,3,4]
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