Project Description


Airborne remote sensing data capture optical properties of the land surface during peak growing season and snow-covered periods, providing information on vegetation composition, canopy chemistry and surface albedo. These data are complementary to those from the High Intensity Terrestrial and Aquatic Networks and the Eddy Flux Tower Network, providing a basis from which the impacts on terrestrial ecosystems and ecosystems services that result from changes in climate, land use and demographics can be examined and quantified.

Project Description

Airborne remote sensing data were acquired specifically for the NH Ecosystems and Society project to provide vegetation biometric and land surface optical properties at the landscape-scale. Data were acquired for targeted field sites that include the Lamprey River Watershed, the Hubbard Brook Experimental Forest and the Bartlett Experimental Forest, where soil and aquatic sensors are deployed and intensive field sample plots have been established to measure a range of vegetation and land surface properties. Two image data collection campaigns were deployed—one in summer (August 2012) to capture peak growing season conditions in the state, and one in winter (Feb/March 2013).

The NH EPSCoR Airborne Imagery provides a mechanism for scaling data from the High Intensity Terrestrial and Aquatic Networks, Eddy Flux Networks and other field observations that are primarily point-measurements to landscape scales. For instance, relationships between field measurements and the hyperspectral data will be used to calculate spatially explicit estimates of canopy albedo and foliar nitrogen concentrations, and to aid land use/land cover mapping for entire watersheds. Relating field measurements to remote sensing data will facilitate analyses of ecosystem services at sites with different land use—e.g. forest, agriculture, residential—at multiple scales. The watershed-level airborne imagery will also be scaled up to broader spatial and temporal ranges by linking them to satellite sensor data such as those from Landsat and MODIS, thereby serving as a scalar to larger geographic regions.  Data derived from remote sensing and eddy covariance measurements will be used by terrestrial and hydrologic modeling activities designed to predict terrestrial carbon, nitrogen and water cycles under past, present and future conditions, as well as in assessments of the impact of future land-use scenarios on local climate.

Project Background

Because plants selectively absorb and reflect light, imaging spectroscopy, or hyperspectral remote sensing, provides an invaluable tool for making observations relevant to ecosystem functioning across the landscape. Relationships between plant traits and canopy reflectance features captured from remote sensing platforms have been derived through the use of multiple regression and other least-squares statistical methods (e.g. Wessman et al. 1988; Martin et al. 2008) and spectral vegetation indices (e.g. Chappelle et al. 1992; Lichtenthaler et al. 1996). Imaging spectroscopy has been used to explore and understand spectral features related to leaf and canopy chemistry, light use efficiency, primary productivity and biophysical variables that influence climate. (For further reading on reflectance and plant traits, see Ollinger 2011.)