Estimation of grassland CO2 exchange rates using hyperspectral remote sensing techniques

Although the link between CO2 exchange and spectral reflectance is well established in laboratory conditions, limited research has been conducted in the field. To determine the applications of remote sensing in the estimation of the northern mixed grasslands as a carbon sink, the primary objective of this study was to evaluate several narrow‐band vegetation indices and band depth analysis in the prediction of leaf CO2 exchange rates in a northern mixed grass prairie ecosystem. Spectral reflectance and CO2 exchange measurements were collected from 13 sites located in Grasslands National Park, Saskatchewan, Canada. Pearson's correlation found a significant relationship between the CO2 exchange rates and the Photochemical Reflectance Index (PRI). Linear regression showed that the PRI explained 46% of the variance seen in the leaf CO2 exchange rates. This is somewhat lower than previous experiments conducted in laboratory conditions; however, the current study was conducted in field conditions, where there are a number of different species in the field of view and background effects from soil, litter and dead materials, which are negligible in laboratory conditions.

[1]  Hendrik Poorter.  Interspecific variation in the growth response of plants to an elevated ambient CO2 concentration , 2004, Vegetatio.

[2]  J. Privette,et al.  Impact of Tissue, Canopy, and Landscape Factors on the Hyperspectral Reflectance Variability of Arid Ecosystems , 2000 .

[3]  John A. Gamon,et al.  Detecting biophysical properties of a semi-arid grassland and distinguishing burned from unburned areas with hyperspectral reflectance , 2004 .

[4]  N. Broge,et al.  Deriving green crop area index and canopy chlorophyll density of winter wheat from spectral reflectance data , 2002 .

[5]  A. Skidmore,et al.  Hyperspectral band depth analysis for a better estimation of grass biomass (Cenchrus ciliaris) measured under controlled laboratory conditions , 2004 .

[6]  I. Moyaa,et al.  A new instrument for passive remote sensing : 2 . Measurement of leaf and canopy reflectance changes at 531 nm and their relationship with photosynthesis and chlorophyll fluorescence , 2004 .

[7]  N. Broge,et al.  Comparing prediction power and stability of broadband and hyperspectral vegetation indices for estimation of green leaf area index and canopy chlorophyll density , 2001 .

[8]  Y. Inoue.  >Synergy of Remote Sensing and Modeling for Estimating Ecophysiological Processes in Plant Production , 2003 .

[9]  N. H. Brogea,et al.  Comparing prediction power and stability of broadband and hyperspectral vegetation indices for estimation of green leaf area index and canopy chlorophyll density , 2022 .

[10]  A. Skidmore,et al.  Integrating imaging spectroscopy and neural networks to map grass quality in the Kruger National Park, South Africa , 2004 .

[11]  D. Verseghy,et al.  Modelling CO2 and energy exchanges in a northern semiarid grassland using the carbon- and nitrogen-coupled Canadian Land Surface Scheme (C-CLASS) , 2005 .

[12]  C. Field,et al.  A narrow-waveband spectral index that tracks diurnal changes in photosynthetic efficiency , 1992 .

[13]  J. Gamon,et al.  The photochemical reflectance index: an optical indicator of photosynthetic radiation use efficiency across species, functional types, and nutrient levels , 1997, Oecologia.

[14]  R. Grant,et al.  Climate impact on net ecosystem productivity of a semi-arid natural grassland: modeling and measurement , 2004 .

[15]  Dar A. Roberts,et al.  Modeling spatially distributed ecosystem flux of boreal forest using hyperspectral indices from AVIRIS imagery , 2001 .

[16]  S. Brooks,et al.  Applied Multivariate Statistics for the Social Sciences , 1993 .

[17]  Moon S. Kim,et al.  Estimating Corn Leaf Chlorophyll Concentration from Leaf and Canopy Reflectance , 2000 .

[18]  D. Roberts,et al.  Using Imaging Spectroscopy to Study Ecosystem Processes and Properties , 2004 .

[19]  G. Rondeaux,et al.  Optimization of soil-adjusted vegetation indices , 1996 .

[20]  Craig M. Trotter,et al.  Estimating photosynthetic light-use efficiency using the photochemical reflectance index: variations among species. , 2004, Functional plant biology : FPB.

[21]  C. Körner,et al.  A field study of the effects of elevated CO2 on plant biomass and community structure in a calcareous grassland , 1999, Oecologia.

[22]  Ismael Moya,et al.  A new instrument for passive remote sensing: 2. Measurement of leaf and canopy reflectance changes at 531 nm and their relationship with photosynthesis and chlorophyll fluorescence , 2004 .

[23]  A. Huete.  A soil-adjusted vegetation index (SAVI) , 1988 .

[24]  H. Gausman,et al.  Leaf Reflectance vs. Leaf Chlorophyll and Carotenoid Concentrations for Eight Crops1 , 1977 .

[25]  C. Justice,et al.  A Revised Land Surface Parameterization (SiB2) for Atmospheric GCMS. Part II: The Generation of Global Fields of Terrestrial Biophysical Parameters from Satellite Data , 1996 .

[26]  D. O. Hall,et al.  Climate Change and Productivity of Natural Grasslands , 1991 .

[27]  D. Sims,et al.  Relationships between leaf pigment content and spectral reflectance across a wide range of species, leaf structures and developmental stages , 2002 .

[28]  Alan K. Knapp,et al.  Biomass Production in a Tallgrass Prairie Ecosystem Exposed to Ambient and Elevated CO"2. , 1993, Ecological applications : a publication of the Ecological Society of America.

[29]  R. Coupland,et al.  Natural grasslands : introduction and Western Hemisphere , 1992 .

[30]  F. Csillag,et al.  The Influence of Vegetation Index and Spatial Resolution on a Two-Date Remote Sensing-Derived Relation to C4 Species Coverage , 2001 .