The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Publications Copernicus
Articles | Volume XLII-2/W13
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2/W13, 975–979, 2019
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2/W13, 975–979, 2019

  05 Jun 2019

05 Jun 2019


A. Elsherif1,2, R. Gaulton1, and J. P. Mills1 A. Elsherif et al.
  • 1School of Engineering, Newcastle University, Newcastle upon Tyne, UK
  • 2Faculty of Engineering, Tanta University, Tanta, Egypt

Keywords: ground-based lidar, fuel moisture content, forest wildfires, vegetation

Abstract. Terrestrial laser scanning (TLS) instruments have been widely utilized in measuring vegetation canopy structural parameters, being capable of providing high density point clouds. However, less attention has been paid to using TLS intensity data in estimating vegetation biochemical attributes, and calculating water status metrics, that can help in early detection of vegetation stress and risk of wildfire. Water status metrics, such as the leaf Equivalent Water Thickness (EWT) and the Fuel Moisture Content (FMC), are being commonly estimated from optical remote sensing data. However, such estimates mainly reflect the water status of canopy top and ignore the vertical heterogeneity of water content distribution within the canopy. The estimates are also affected by canopy structure and understory reflectance. Such limitations can potentially be addressed using TLS intensity data, as observations are performed in three dimensions (3D). This study therefore investigated the potential of using dual-wavelength TLS intensity data to estimate FMC in 3D. The calculated Normalized Difference Index (NDI) of 808 nm near infrared and 1550 nm shortwave infrared wavelengths was found to be correlated to FMC at leaf level for four different tree species. The correlation was moderate, and the relationships were not consistent between species. NDI was subsequently used to estimate FMC at canopy level in seven trees in a small tree plot with an average error < 5 %. The 3D estimates of FMC revealed vertical heterogeneity in all trees measured, which varied between species and also between trees from the same species.