Do BRDF effects dominate seasonal changes in tower-based remote sensing imagery?

Friday, 19 December 2014
Jyoteshwar R Nagol1, Douglas C Morton2, Jeremy Rubio3, Bruce D Cook2 and Khaldoun Rishmawi4, (1)University of Maryland College Park, College Park, MD, United States, (2)NASA Goddard Space Flight Center, Greenbelt, MD, United States, (3)Centre d'Etudes Spatiales de la Biosphere, Toulouse Cedex 9, France, (4)University of Maryland College Park, Geographical Sciences, College Park, MD, United States
In situ remote sensing complements data from airborne and space-based sensors, in particular for intensive study sites where optical imagery can be paired with detailed ground and tower measurements. The characteristics of tower-mounted imaging systems are quite different from the nadir viewing geometry of other remote sensing platforms. In particular, tower-mounted systems are quite sensitive to artifacts of seasonal and diurnal sun angle variations. Most systems are oriented in a fixed north or south direction (depending on latitude), placing them in the principal plane at solar noon. The strength of the BRDF (Bidirectional Reflectance Distribution Function) effect is strongest for images acquired at that time. Phenological metrics derived from tower based oblique angle imaging systems are particularly prone to BRDF effects, as shadowing within and between tree crowns varies seasonally. For sites in the northern hemisphere, the fraction of sunlit and shaded vegetation declines following the June solstice to leaf senescence in September.

Correcting tower-based remote sensing imagery for artifacts of BRDF is critical to isolate real changes in canopy phenology and reflectance. Here, we used airborne lidar data from NASA Goddard’s Lidar, Hyperspectral, and Thermal Airborne Imager (G-LiHT) to develop a 3D forest scene for Harvard Forest in the Discrete Anisotrophic Radiative Transfer (DART) model. Our objective was to model the contribution of changes in shadowing and illumination to observations of changes in greenness from the Phenocam image time series at the Harvard Forest site.  Diurnal variability in canopy greenness from the Phenocam time series provides an independent evaluation of BRDF effects from changes in illumination and sun-sensor geometries.

The overall goal of this work is to develop a look-up table solution to correct major components of BRDF for tower-mounted imaging systems such as Phenocam, based on characteristics of the forest structure (forest height, canopy rugosity, fractional cover, and composition) and viewing geometry of the sensor. Given the sensitivity of tower-based systems to BRDF effects, efforts to correct artifacts of BRDF in phenology time series is critical to isolate seasonal changes in vegetation reflectance.