NASA to Fund Study of Tree Canopy Impact on Remote Sensing Measurements

COLUMBUS, Ohio (Oct. 13, 2020)—NASA has announced it will fund a three-year collaborative project to investigate how the chemical properties of leaves and tree canopy structures affect remote sensing observations. The research, backed by NASA’s Research Opportunities in Space and Earth Science program, will be led by scientists from Battelle, the Rochester Institute of Technology, and the University of New Hampshire using data from the National Ecological Observatory Network (NEON) program.  

“The goal of the research is to understand the linkages between canopy structure and the leaf traits that influence photosynthetic capacity,” said NEON’s Keith Krause, who is leading the research. “This should allow us to improve our interpretation of remote sensing data and to better map and monitor rates of productivity in forest ecosystems.”

The combination of NEON data and NASA satellite technology is key. The research methods include the use of multi-modal remote sensing with passive solar-reflected hyperspectral and active discrete/waveform LiDAR data to study structure and trait relationships across a wide range of spatial resolutions. The NEON data are collected at high spatial resolution delivering detail at a scale smaller than individual tree canopies but are limited to regions centered on each NEON site. Combining it with NASA’s satellite technology will provide a more thorough picture of the structure/trait relationships across the continent and globe.

The biochemistry, physiology, and orientation of foliage is a key driver of productivity in forests and has spawned multiple leaf trait remote sensing studies. By studying the ecosystem and canopy structure, researchers can learn how trees grow, how trees provide different habitats for various animals, and what factors affect how trees “breathe” and exchange carbon dioxide with the atmosphere. In addition, having a better understanding of baseline vegetation chemistry and forest health, for example, can help in the early detection of invasive species.

Although these studies have demonstrated that leaf traits such as foliar nitrogen concentration, leaf mass per unit area, and associated patterns of forest growth can be estimated from remotely-sensed data, the mechanisms linking vegetation chemistry, structure, and function remain unclear. This is largely because in whole canopies, leaf-level reflectance properties are confounded by the influence of structural variables at the leaf, branch, and whole-crown scales. This produces tremendous complexity in leaf surfaces and canopy gaps, while also affecting radiative transfer through forest canopies.

Researchers will use two existing NEON sites for the study —the Harvard Forest in Massachusetts and the Bartlett Experimental Forest in New Hampshire. Field measurements and remote sensing data from the sites will be augmented with simulations of realistic forests where trade studies will be performed to better understand how changes in leaf chemistry and canopy structure affect the remote sensing signals.

Battelle has operated NEON for the National Science Foundation since 2016. NEON collects long-term ecological data to better understand how ecosystems are changing across the United States. The open access comprehensive data, spatial extent, and remote sensing technology provided through the NEON program is enabling a large and diverse user community to tackle new questions at scales not accessible to previous generations of ecologists.

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Oct 13, 2020


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