OOS 35-2 - Global imaging spectroscopy unlocks the potential for measuring leaf economics traits (almost) everywhere and (almost) all of the time

Wednesday, August 9, 2023

8:15 AM – 8:30 AM PDT

Location: 252

Presenting Author(s)

PT

Phil PA Townsend, Ph.D.

Professor
University of Wisconsin - Madison
Madison, Wisconsin, United States

Co-author(s)

KC

K. Dana Chadwick

Jet Propulsion Laboratory, California Institute of Technology, California, United States
AC

Adam Chlus

CalTech Jet Propulsion Laboratory
PD

Phuong Dao

Postdoctoral Research Associate
Department of Forest and Wildlife Ecology, University of Wisconsin-Madison, United States
BD

Benjamin Dechant

German Centre for Integrative Biodiversity Research (iDiv)
KK

Kyle R. Kovach

Postdoctoral Research Associate
University of Wisconsin
Madison, Wisconsin, United States
RP

Ryan P. Pavlick

NASA JPL
NQ

Natalie Queally

PhD Student
University of Wisconsin-Madison, Wisconsin, United States
FS

Fabian Daniel Schneider

Jet Propulsion Laboratory / California Institute of Technology
Pasadena, California, United States
ZY

Zhiwei Ye

University of Wisconsin-Madison
TZ

Ting Zheng

University of Wisconsin-Madison
AT

Amy M. Trowbridge

Assistant Professor
University of Wisconsin - Madison
Madison, Wisconsin, United States
CO

Celso R. Oliveira

University of Wisconsin - Madison, United States

Imaging spectroscopy has emerged as a viable tool for characterizing vegetation functional traits at broad spatial scales. These advances result from major efforts such as the US National Ecological Observatory Network (NEON), the Global Airborne Observatory (GAO) and surveys by agencies such as NASA and the European Space Agency. Traits that are commonly derived from hyperspectral remote sensing include leaf mass per area, and foliar concentrations of nitrogen, pigments, lignin and cellulose, nonstructural carbohydrates and defensive compounds. Integration of maps of these traits with environmental data enables assessment of drivers of trait variation. Because many traits are derived concurrently from imagery, we are also able to better understand trait-trait relationships across broad scales. Traits from imaging spectroscopy provide both a substitute for and supplement to in-situ measurements and information typically drawn from databases.

Forthcoming satellite missions with global seasonal coverage will facilitate new insights into our current understanding of the leaf economics spectrum, i.e. the apparent tradeoffs between photosynthetic capacity, leaf construction costs, defense and maintenance. Using airborne data from multiple sources as well as spaceborne data from PRISMA, we demonstrate that there is significant nuance in spatial and temporal patterns of foliar traits, at multiple scales. Notably patterns that are observed broadly can be reversed at local scales, change seasonally, or even change across years in response to climatic variation such as drought or in response to herbivory. However, we can also use the data to confirm broad-scale relationships hypothesized from limited in-situ data sets, such as a pattern of higher foliar defensive compounds in forests at lower latitudes. The notable contribution of imaging spectroscopy to trait-based ecology is the new capacity to ask questions and test hypotheses that are not feasible using traditional methods.

Global-scale trait-based ecology using remote sensing faces numerous challenges. For example, the pixel resolution of imagery is generally larger than the plants in an ecosystem, so each pixel represents traits for multiple individual plants and species. As well, remote sensing data only captures traits of the sunlit canopy (SC). The questions arise: What do traits of a pixel mean, ecologically? Can we scale SC information through the depth of a canopy and make inferences about the understory? We address these questions through a series of examples from local-to-global scales, and provide a roadmap to the work that is needed to best utilize the forthcoming deluge of trait data from imaging spectroscopy.