Retrieving plant traits
Concerns over climate change, human-caused disturbances, and land-use effects on ecosystems have made it critical to quantify and characterize ecosystem functions, such as nutrient cycling, litter decomposition, and plant productivity. Knowledge and understanding of these functions allow us to assess the health of an ecosystem. Plant traits play an important role in controlling these functions, which makes measurements of plant traits highly valuable. However, traditional methods of collecting and processing extensive measurements of plant traits through time are expensive and time consuming. Using relationships derived between spectra (i.e., spectroscopy) and laboratory measured leaf traits can decrease processing time through faster analytical speed and minimal sample preparation. These relationships once derived using field plots or ground-based measurements can be used in conjunction with imaging spectroscopy to further increase spatial and temporal sampling.
Vegetation traits provide critical information on ecosystem function that can be used to assess the effects of disturbance, land use, and climate change. Recent studies have demonstrated the use of spectroscopy to predict vegetation traits accurately and efficiently. To date, most spectroscopic studies have utilized data from the Visible Short Wave Infrared spectrum (VSWIR) or, occasionally, the Thermal Infrared spectrum (TIR), but not in combination. This study focused on VSWIR and TIR synergy to evaluate the ability to predict leaf level cellulose, lignin, leaf mass per area (LMA), nitrogen, and water content across seasons. We used fresh leaves from sixteen common California shrub and tree species collected in the 2013 spring, summer, and fall seasons. For each leaf trait, partial least squares regression (PLSR) models were fit using different portions of the spectrum: VSWIR (0.35–2.5 μm), TIR (2.5–15.4 μm), and Full spectrum (0.35–15.4 μm). We also fit PLSR models using spectra resampled to simulate three airborne sensors: the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS; 0.4–2.5 μm), the Hyperspectral Thermal Emission Spectrometer (HyTES; 7.5–12 μm), and the Hyperspectral InfraRed Imager (HyspIRI; 0.4–12 μm). General models successfully captured the variability among all seasons and leaf forms for cellulose and water content, while the other leaf traits were better modeled with season or leaf form-specific models. This study successfully captured the large seasonal and geographical variation in leaf traits across California's diverse ecosystems, supporting the possibility of using HyspIRI's imagery for global mapping efforts of these traits.
Annual June-July precipitation (top panel) and frequency of wet days (bottom panel) for a weather station near Lakeside Lab. Black lines show the average (A) since 1980. Red lines show the proposed drought (D) and low frequency (L) treatments.
Starting in Spring 2022, we will examine bur oak responses to a combination of drought and high precipitation variability using precipitation manipulation experiments at Iowa’s Lakeside Laboratory. We will test the hypothesis that increased precipitation variability decreases photosynthetic capacity and growth through precipitation manipulation experiments of bur oak saplings. Since the experimental manipulations are necessarily limited in spatial and temporal scope, we will simultaneously collect leaf hyperspectral reflectance using an ASD FieldSpec 4 to examine the potential of remote sensing to provide detection and early warning of plant stress, which would allow scaling of these experimental results to the broader Midwest region. Using the coincident physiological measurements, we will develop models to predict plant physiological properties based on hyperspectral signatures. These analyses will provide new insights into: i) how and why Midwestern forests respond to enhanced hydroclimatic intensity, and ii) detection of these responses in real-time using cutting edge remote sensing techniques, thus facilitating management and mitigation of forest stress in a changing climate.
Meerdink, S.K., Roberts, D.A., King, J. Y., Roth, K. L., Dennison, P. E., Amaral, C. H., & Hook, S. J. (2016). Linking seasonal foliar traits to VSWIR-TIR spectroscopy across California ecosystems. Remote Sensing of Environment, 186, 322-338.