Journal article
Linking seasonal foliar traits to VSWIR-TIR spectroscopy across California ecosystems
Remote sensing of environment, Vol.186, pp.322-338
12/01/2016
DOI: 10.1016/j.rse.2016.08.003
Abstract
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 focuses 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. The 284 samples exhibited a wide range of leaf traits as determined by standard analytical procedures: 4.2–27.3% for cellulose, 2.6–22.5% for lignin, 34.7–388.9g/m2 for LMA, 0.45–3.81% for nitrogen, and 20.2–76.9% for water content. For each leaf trait, partial least squares regression (PLSR) models were fit using different portions of the spectrum: VSWIR (0.35–2.5μm), TIR (2.5–15.4μm), and Full spectrum (0.35–15.4μm). We also fit PLSR models using spectra resampled to simulate three airborne sensors: the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS; 0.4–2.5μm), the Hyperspectral Thermal Emission Spectrometer (HyTES; 7.5–12μm), and the Hyperspectral InfraRed Imager (HyspIRI; 0.4–12μm). The majority of best performing models used the Full spectrum, demonstrating the value of combining TIR and VSWIR spectra for leaf trait prediction. Sensor-simulated PLSR models created with the entire data set yielded validation R2 and root mean square error of prediction (RMSEP) values as follows: R2=0.70 and RMSEP=13.1% for cellulose, R2=0.50 and RMSEP=17.7% for lignin, R2=0.56 and RMSEP=18.3% for LMA, R2=0.56 and RMSEP=18.1% for nitrogen, and R2=0.89 and RMSEP=5.7% for water content. 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.
•We modeled different spectral region's ability to predict seasonal leaf traits.•Laboratory Full spectrum models have increased precision compared to VSWIR or TIR.•Simulated HyspIRI models also had increased precision compared to AVIRIS or HyTES.•General models captured all seasonal variation for cellulose, nitrogen, and water.•VSWIR+TIR spectroscopy improves prediction on large spatial and temporal scales.
Details
- Title: Subtitle
- Linking seasonal foliar traits to VSWIR-TIR spectroscopy across California ecosystems
- Creators
- Susan K Meerdink - University of California, Santa BarbaraDar A Roberts - University of California, Santa BarbaraJennifer Y King - University of California, Santa BarbaraKeely L Roth - University of California, DavisPhilip E Dennison - University of UtahCibele H Amaral - Universidade Federal de ViçosaSimon J Hook - California Institute of Technology
- Resource Type
- Journal article
- Publication Details
- Remote sensing of environment, Vol.186, pp.322-338
- Publisher
- Elsevier Inc
- DOI
- 10.1016/j.rse.2016.08.003
- ISSN
- 0034-4257
- eISSN
- 1879-0704
- Grant note
- DOI: 10.13039/100000104, name: NASA, award: NNX12AP08G
- Language
- English
- Date published
- 12/01/2016
- Academic Unit
- Geographical and Sustainability Sciences
- Record Identifier
- 9984259386802771
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