Estimating Forest Canopy Height Using MODIS BRDF Data Emphasizing Typical-Angle Reflectances

Abstract

Forest-canopy height is an important parameter for the estimation of forest biomass and terrestrial carbon flux and climate-change research at regional and global scales. Currently, various methods combining Light Detection and Ranging (LiDAR) data with various auxiliary data, particularly satellite remotely sensed reflectances, have been widely used to produce spatially continuous canopy-height products. However, current methods in use for remote sensing reflectances mainly focus on the nadir view direction, while anisotropic reflectances, which are theoretically more sensitive to the forest canopy height in the multiangle remote sensing field, have rarely been explored. Here, we attempted to examine the potential of using modeled multiangle reflectances at three typical viewing angles (i.e., from the hotspot, darkspot, and nadir directions) to estimate forest-canopy height as auxiliary data sources. First, the sensitivities of the typical angular reflectances as a function of forest canopy height were fully examined using the Extended Fourier Amplitude Sensitivity Test (EFAST) method based on the 4-scale Bidirectional Reflectance Distribution Function (BRDF) model simulations. This indicated that reflectances in the off-nadir viewing directions are generally sensitive to canopy-height variations. Then, the canopy heights were extracted from airborne Laser Vegetation Imaging Sensor (LVIS) data, which were further divided into training and validation data. Moderate Resolution Imaging Spectroradiometer (MODIS) multiangle reflectances at typical viewing angles were calculated from the MODIS BRDF parameter product (MCD43A1, version 6) as partial training-input data, based on a hotspot-adjusted, kernel-driven linear BRDF model. Subsequently, the Random Forest (RF) machine learning model was trained to acquire the relationship between the extracted canopy heights and the corresponding MODIS typical viewing reflectances. The trained model was further applied to estimate the canopy height metrics in the study areas of Howland Forest, Harvard Forest, and Bartlett Forest. Finally, the estimated canopy heights were independently validated by canopy heights extracted from the LVIS data. The results indicate that the canopy heights modeled through this method exhibit generally high accordance with the LVIS-derived canopy heights (R = 0.65−0.67; RMSE = 3.63−5.78). The results suggest that the MODIS multiangle reflectance data at typical observation angles contain important information regarding forest canopy height and can, therefore, be used to estimate forest canopy height for various ecological applications.

Keywords

• multiangular reflectances
• brdf
• kernel-driven brdf model
• lvis
• canopy height
• hotspot effect
• darkspot
• lidar
• random forest machine learning model