3D leaf water content mapping using terrestrial laser scanner backscatter intensity with radiometric correction

Leaf water content (LWC) plays an important role in agriculture and forestry management. It can be used to assess drought conditions and wildfire susceptibility. Terrestrial laser scanner (TB) data have been widely used in forested environments for retrieving geometrically-based biophysical parameters. Recent studies have also shown the potential of using radiometric information (backscatter intensity) for estimating LWC. However, the usefulness of backscatter intensity data has been limited by leaf surface characteristics, and incidence angle effects. To explore the idea of using LiDAR intensity data to assess LWC we normalized (for both angular effects and leaf surface properties) shortwave infrared TLS data (1550 nm). A reflectance model describing both diffuse and specular reflectance was applied to remove strong specular backscatter intensity at a perpendicular angle. Leaves with different surface properties were collected from eight broadleaf plant species for modeling the relationship between LWC and backscatter intensity. Reference reflectors (Spectralon from Labsphere, Inc.) were used to build a lookup table to compensate for incidence angle effects. Results showed that before removing the specular influences, there was no significant correlation (R-2 = 0.01, P > 0.05) between the backscatter intensity at a perpendicular angle and LWC. After the removal of the specular influences, a significant correlation emerged (R-2 = 0.74, P < 0.05). The agreement between measured and TLS-derived LWC demonstrated a significant reduction of RMSE (root mean square error, from 0.008 to 0.003 g/cm(2)) after correcting for the incidence angle effect. We show that it is possible to use TLS to estimate LWC for selected broad-leaved plants with an R-2 of 0.76 (significance level alpha = 0.05) at leaf level. Further investigations of leaf surface and internal structure will likely result in improvements of 3D LWC mapping for studying physiology and ecology in vegetation. (C) 2015 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS). Published by Elsevier B.V. All rights reserved.