Elsevier

Remote Sensing of Environment

Volume 173, February 2016, Pages 174-186
Remote Sensing of Environment

Examining spectral reflectance features related to foliar nitrogen in forests: Implications for broad-scale nitrogen mapping

https://doi.org/10.1016/j.rse.2015.11.028Get rights and content

Highlights

  • We explore the effects of sensor characteristics on forest %N estimates.

  • We compare forest canopy %N and reflectance data from AVIRIS, Landsat and MODIS.

  • Results suggest promise for broadscale canopy %N estimation with a variety of sensors.

Abstract

The concentration of nitrogen (N) in foliage often limits photosynthesis and can influence a number of important biogeochemical processes. For this reason, methods for estimating foliar %N over a range of scales are needed to enhance understanding of terrestrial carbon and nitrogen cycles. High spectral resolution aircraft remote sensing has become an increasingly common tool for landscape-scale estimates of canopy %N because reflectance in some portions of the spectrum has been shown to correlate strongly with field-measured %N. These patterns have been observed repeatedly over a wide range of biomes, opening new possibilities for planned Earth observation satellites. Nevertheless, the effects of spectral resolution and other sensor characteristics on %N estimates have not been fully examined, and may have implications for future analyses at landscape, regional and global scales. In this study, we explored the effects of spectral resolution, spatial resolution and sensor fidelity on relationships between forest canopy %N and reflectance measurements from airborne and satellite platforms. We conducted an exercise whereby PLS, simple and multiple regression calibrations to field-measured canopy %N for a series of forested sites were iteratively performed using (1) high resolution data from AVIRIS (Airborne Visible/Infrared Imaging Spectrometer) that were degraded spectrally from 10 nm to 30 nm, 50 nm, 70 nm, and 90 nm bandwidths, and spatially from 18 m to 30 m and 60 m pixels; (2) data representing Landsat and MODIS (Moderate Resolution Imaging Spectroradiometer) spectral bands simulated with data from AVIRIS; and (3) actual data from Landsat and MODIS. We observed virtually no reduction in the strength of relationships between %N and reflectance when using coarser bandwidths from AVIRIS, but instead saw declines with increasing spatial resolution and loss of sensor fidelity. This suggests that past efforts to examine foliar %N using broad-band sensors may have been limited as much by the latter two properties as by their coarser spectral bandwidths. We also found that regression models were driven primarily by reflectance over broad portions of the near infrared (NIR) region, with little contribution from the visible or mid infrared regions. These results suggest that much of the variability in canopy %N is related to broad reflectance properties in the NIR region, indicating promise for broad scale canopy N estimation from a variety of sensors.

Introduction

The concentration of nitrogen (N) in foliage is linked to numerous biogeochemical, physiological and ecological processes and serves as a useful indicator of ecosystem metabolism. Foliar N has been repeatedly identified as a useful predictor of photosynthetic capacity, or Amax (Evans, 1989, Field and Mooney, 1986, Reich et al., 1999, Wright et al., 2004); it has been related to stand-level processes such as net primary production (NPP) and canopy light use efficiency (Green et al., 2003, Kergoat et al., 2008, Smith et al., 2002); it provides a widely used measure of herbivore forage quality and susceptibility to defoliation (Jefferies et al., 1994, Mattson, 1980, Peeters, 2002); and can provide direct input to ecosystem models (Ollinger and Smith, 2005, Wythers et al., 2005).

In addition to its influence on carbon assimilation, foliar %N is also tied to the availability of N in soils through mechanisms involving litter decay, net mineralization and plant N uptake (Merilä and Derome, 2008, Ollinger et al., 2002, Parton et al., 2007). This is important given the degree to which humans have perturbed the N cycle globally (e.g., Galloway et al., 2003), and the tendency for N to limit productivity in terrestrial ecosystems (Jandl et al., 2007, Vitousek and Howarth, 1991).

Despite its many important roles, foliar %N is rarely used as a driver in regional- to global-scale analyses. This is, in part, because we lack a reliable means of extending foliar N field measurements to broad-scale spatial patterns. At finer scales (~ 100–1000 km2), the capacity for foliar N estimation has been repeatedly demonstrated using high spectral resolution remote sensing instruments, or imaging spectrometers (e.g., Asner and Vitousek, 2005, Coops et al., 2003, Martin and Aber, 1997, McNeil et al., 2008, Ollinger and Smith, 2005, Smith et al., 2003, Townsend et al., 2003, Wessman et al., 1988), whose narrow bands can record spectral features that result from electron transitions in pigments and/or are associated with other biochemical constituents in foliage (e.g., Curran, 1989, Curran, Kupiec, and Smith, 1997, Kokaly and Clark, 1999). Martin, Plourde, Ollinger, Smith, and McNeil (2008) further demonstrated that relationships between field-measured %N and canopy spectral properties were strongly driven by NIR reflectance patterns, and were consistent enough across boreal, temperate and tropical forests, to allow development of a single, generalized partial least squares (PLS) equation. Still, application of these methods has been limited because presently available imaging spectrometers have swath widths in the range of 10 km or less and because the potential for similar approaches using other instruments has not been thoroughly tested.

There are at least two potential solutions to this problem. The first is development of a space-based imaging spectrometer capable of providing regional to global coverage. Although planning for such instruments is underway (e.g., HyspIRI; Chien et al., 2009, Council, 2007), it will likely be years before data become routinely available. A second possibility is to evaluate the degree to which foliar %N might also be estimated using spectral features available from existing sensors that provide broader spatial coverage. Potential for this approach was suggested by Ollinger et al. (2008) and Ollinger (2011) who observed that reflectance over broad portions of the NIR region was strongly correlated with measured %N in temperate and boreal forests, and by Martin et al. (2008) whose generalized partial least squares %N model was most heavily influenced by reflectance across the NIR plateau from 750 nm to 1250 nm. Although there have been other indications that broad-band spectral features contain information related to variability in canopy N (Gamon et al., 1995, Hollinger et al., 2010, Zhao et al., 2005), no studies to date have focused explicitly on how variability in spectral resolution, spatial resolution and sensor fidelity affect foliar N estimation capabilities.

In this study, we examined the influence of spectral resolution, spatial resolution and sensor fidelity on relationships between observed patterns of foliar %N and canopy reflectance. Sensor fidelity refers to the combination of signal-to-noise ratio (SNR), detector uniformity and stability of electronics in an imaging system that together affect the quality of spectra (e.g., Asner et al., 2007, Chen et al., 2012, Kokaly et al., 2009, Mouroulis and McKerns, 2000). Our analysis draws on a combination of remote sensing and coordinated field measurements from 155 plots within 13 forested research sites across North America. Field measurements were used to evaluate imaging spectrometer data from AVIRIS (high spectral resolution, high spatial resolution, high sensor fidelity); broad-scale sensor data from MODIS (moderate spectral resolution, coarse spatial resolution, high sensor fidelity) and broad-band data from Landsat 5 (coarse spectral resolution, moderate spatial resolution, moderate data fidelity). We also examined relationships between field-measured foliar %N and reflectance in visible and infrared wavelengths on their own, and in several commonly used vegetation indices, in order to evaluate their potential roles in future studies of %N estimation.

Section snippets

Study sites and field data collection

Our analysis used an existing collection of data from several previous investigations (Ollinger, 2011, Ollinger et al., 2008, Martin et al., 2008) that included thirteen North American research sites representing temperate and boreal evergreen needleleaf and deciduous broadleaf and mixed forests, spanning a range of ages. Site descriptions and sampling dates are given in Table 1.

At each site, samples of sunlit canopy foliage were collected from eight to twenty ~ 20 × 20 m plots, generally located

Spectral resolution

The multi-site PLS regression of field-measured, whole-canopy %N with AVIRIS reflectance at the nominal 10 nm spectral resolution (i.e., the benchmark model) resulted in a strong, highly significant relationship (r2 = 0.86, PRESS RMSE = 0.21 and RMSE = 0.19; Table 3a, Fig. 1a) and produced a predicted versus observed trend that did not differ significantly from the 1:1 line. Predicted and observed %N values were lowest in evergreen-dominated forests, highest in deciduous forests and intermediate in

Discussion

The purpose of this study was to evaluate how observed relationships between spectral reflectance and forest canopy %N vary as a function of sensor properties, with the goals of furthering our understanding of factors affecting reflectance and advancing approaches to estimating spatial patterns in %N. As with any empirically based method of remote sensing, results should be applied only within the domain of the data used to generate them and not, for example, extended to tropical forests,

Conclusions

Results from regressions of AVIRIS reflectance on canopy N from 13 forested sites in North America suggest that general methods for %N estimation over broad spatial scales should be possible with future high-fidelity global imaging spectrometers as well as high-fidelity broad-band sensors. We saw little loss of accuracy when AVIRIS spectra were degraded to coarse bandwidths, suggesting that some of the synergy captured with narrowband data is also captured by broad-band sensors. The strength of

Acknowledgments

Funding and AVIRIS data were provided by NASA in support of the following grants: NASA Carbon Cycle Science Award #NNX08AG14G and #NNX14AJ18G, and NASA Terrestrial Ecology Award #NNX11AB88G. Portions of this research were also supported through the Harvard Forest and Hubbard Brook Long Term Ecological Research programs (NSF 1237491 and 1114804, respectively) and the USDA Forest Service, Northeastern States Research Cooperative (Award #15-DG-11242307-053). We also acknowledge the Canadian Carbon

References (96)

  • R.F. Kokaly et al.

    Spectroscopic determination of leaf biochemistry using band-depth analysis of absorption features and stepwise multiple linear regression

    Remote Sensing of Environment

    (1999)
  • R.F. Kokaly et al.

    Characterizing canopy biochemistry from imaging spectroscopy and its application to ecosystem studies

    Remote Sensing of Environment

    (2009)
  • M.E. Martin et al.

    A generalizable method for remote sensing of canopy nitrogen across a wide range of forest ecosystems

    Remote Sensing of Environment

    (2008)
  • Ü. Niinemets et al.

    Modification of light-acclimation of Pinus sylvestris shoot architecture by site fertility

    Agricultural and Forest Meteorology

    (2002)
  • R.O. Pacumbaba et al.

    Changes in hyperspectral reflectance signatures of lettuce leaves in response to macronutrient deficiencies

    Advances in Space Research

    (2011)
  • D.R. Peddle et al.

    Large area forest classification and biophysical parameter estimations using the 5-Scale canopy reflectance model in Multiple-Forward-Mode

    Remote Sensing of Environment

    (2004)
  • J. Pontius et al.

    Using AVIRIS to assess hemlock abundance and early decline in the Catskills, New York

    Remote Sensing of Environment

    (2005)
  • M.J. Pringle et al.

    Geostatistical interpolation of SLC-off Landsat ETM plus images

    ISPRS Journal of Photogrammetry and Remote Sensing

    (2009)
  • D.P. Roy et al.

    Landsat-8: Science and product vision for terrestrial global change research

    Remote Sensing of Environment

    (2014)
  • C.B. Schaaf et al.

    First operational BRDF, albedo nadir reflectance products from MODIS

    Remote Sensing of Environment

    (2002)
  • L. Suárez et al.

    Assessing canopy PRI for water stress detection with diurnal airborne imagery

    Remote Sensing of Environment

    (2008)
  • Q. Zhang et al.

    Characterization of seasonal variation in forest canopy in a temperate deciduous broadleaf forest, using daily MODIS data

    Remote Sensing of Environment

    (2006)
  • C.J. Zhao et al.

    Predicting grain protein content of winter wheat using remote sensing data based on nitrogen status and water stress

    International Journal of Applied Earth Observation and Geoinformation

    (2005)
  • J.D. Aber

    Foliage-height profiles and succession in northern hardwood forests

    Ecology

    (1979)
  • J.D. Aber

    A method for estimating foliage-height profiles in broad-leaved forests

    Journal of Ecology

    (1979)
  • G.P. Asner et al.

    Remote analysis of biological invasion and biogeochemical change

    Proceedings of the National Academy of Sciences of the USA

    (2005)
  • G.P. Asner et al.

    Carnegie Airborne Observatory: In-flight fusion of hyperspectral imaging and waveform light detection and ranging (wLIDAR) for three-dimensional studies of ecosystems

    Journal of Applied Remote Sensing

    (2007)
  • K.L. Bolster et al.

    Determination of carbon fraction and nitrogen concentration in tree foliage by near infrared reflectance: A comparison of statistical methods

    Canadian Journal of Forest Research

    (1996)
  • Y. Chen et al.

    Computation of signal-to-noise ratio of airborne hyperspectral imaging spectrometer

  • S. Chien et al.

    Onboard science processing concepts for the HyspIRI mission

    IEEE Intelligent Systems

    (2009)
  • M.A. Cho et al.

    Evaluating variations of physiology-based hyperspectral features along a soil water gradient in a Eucalyptus grandis plantation

    International Journal of Remote Sensing

    (2010)
  • D.C. Close et al.

    Leaf angle responds to nitrogen supply in eucalypt seedlings. Is it a photoprotective mechanism?

    Tree Physiology

    (2006)
  • M.A. Cochrane

    Using vegetation reflectance variability for species level classification of hyperspectral data

    International Journal of Remote Sensing

    (2000)
  • Y. Cohen et al.

    Interactions among nitrogen, carbon, plant shape, and photosynthesis

    The American Naturalist

    (1996)
  • N.C. Coops et al.

    Prediction of eucalypt foliage nitrogen content from satellite-derived hyperspectral data

    IEEE Transactions on Geoscience and Remote Sensing

    (2003)
  • National Research Council. (2007). Earth science and applications from space: National imperatives for the next decade...
  • P.J. Curran et al.

    Remote sensing the biochemical composition of a slash pine canopy

    IEEE Transactions on Geoscience and Remote Sensing

    (1997)
  • A.J. Elmore et al.

    Precision and accuracy of EO-1 Advanced Land Imager (ALI) data for semiarid vegetation studies

    IEEE Transactions on Geoscience and Remote Sensing

    (2003)
  • J.R. Evans

    Photosynthesis and nitrogen relationships in leaves of C3 plants

    Oecologia

    (1989)
  • J. Ferwerda et al.

    Nitrogen detection with hyperspectral normalized ratio indices across multiple plant species

    International Journal of Remote Sensing

    (2005)
  • C. Field et al.

    The photosynthesis-nitrogen relationship in wild plants

  • A. Fischer et al.

    Adapting the use of hyperspectral imagery in ocean process studies

  • J.N. Galloway et al.

    The nitrogen cascade

    Bioscience

    (2003)
  • J.A. Gamon et al.

    Relationships between NDVI, canopy structure and photosynthesis in three Californian vegetation types

    Ecological Applications

    (1995)
  • U.S. Geological Survey

    Earth Resources Observation and Science (EROS) Center

  • B. Gil-Pérez et al.

    Remote sensing detection of nutrient uptake in vineyards using narrow-band hyperspectral imagery

    Vitis

    (2010)
  • D.G. Goodenough et al.

    Processing Hyperion and ALI for forest classification

    IEEE Transactions on Geoscience and Remote Sensing

    (2003)
  • D.S. Green et al.

    Foliar morphology and canopy nitrogen as predictors of light-use efficiency in terrestrial vegetation

    Agricultural and Forest Meteorology

    (2003)
  • Cited by (59)

    View all citing articles on Scopus
    View full text