Field Imaging Spectroscopy of Beech Seedlings under Dryness Stress

In order to monitor dryness stress under controlled conditions, we set up an experiment with beech seedlings in plant pots and built a platform for observing the seedlings with field imaging spectroscopy. This serves as a preparation for multi-temporal hyperspectral air- and space-borne data expected to be available in coming years. Half of the trees were watered throughout the year; the other half were cut off from water supply for a five-week period in late summer. Plant health and soil, as well as leaf water status, were monitored. Moreover, hyperspectral images of the trees were acquired four times during the experiment. Results show that the experimental imaging setup is well suited for recording hyperspectral images of objects, like the beech pots, under natural illumination conditions. The high spatial resolution makes it feasible to discern between background, soil, wood, green leaves and brown leaves. Furthermore, it could be shown that dryness stress is detectable from an early stage even in the limited spectral range considered. The decline of leaf chlorophyll over time was also well monitored using imaging spectroscopy data.

[1]  B. Gao NDWI—A normalized difference water index for remote sensing of vegetation liquid water from space , 1996 .

[2]  N. Gobron,et al.  The state of vegetation in Europe following the 2003 drought , 2005 .

[3]  M. Schlerf,et al.  Remote sensing of forest biophysical variables using HyMap imaging spectrometer data , 2005 .

[4]  S. Ustin,et al.  Water content estimation in vegetation with MODIS reflectance data and model inversion methods , 2003 .

[5]  Roberta E. Martin,et al.  Spectroscopy of canopy chemicals in humid tropical forests , 2011 .

[6]  Henning Buddenbaum,et al.  Short communication: Laboratory imaging spectroscopy of soil profiles , 2011 .

[7]  D. Sims,et al.  Estimation of vegetation water content and photosynthetic tissue area from spectral reflectance: a comparison of indices based on liquid water and chlorophyll absorption features , 2003 .

[8]  Irene Marzolff,et al.  Unmanned Aerial Vehicle (UAV) for Monitoring Soil Erosion in Morocco , 2012, Remote. Sens..

[9]  Nigel P. Fox,et al.  Progress in Field Spectroscopy , 2006, 2006 IEEE International Symposium on Geoscience and Remote Sensing.

[10]  W. Verhoef,et al.  Coupled soil–leaf-canopy and atmosphere radiative transfer modeling to simulate hyperspectral multi-angular surface reflectance and TOA radiance data , 2007 .

[11]  F. Baret,et al.  PROSPECT: A model of leaf optical properties spectra , 1990 .

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

[13]  A. Barbati,et al.  Climate change impacts, adaptive capacity, and vulnerability of European forest ecosystems , 2010 .

[14]  H. Pleijel,et al.  Evaluating the relationship between leaf chlorophyll concentration and SPAD-502 chlorophyll meter readings , 2007, Photosynthesis Research.

[15]  S. Wold,et al.  PLS-regression: a basic tool of chemometrics , 2001 .

[16]  K. Barry,et al.  Optimizing spectral indices and chemometric analysis of leaf chemical properties using radiative transfer modeling , 2011 .

[17]  Christoph Emmerling,et al.  Determination of total soil organic C and hot water‐extractable C from VIS‐NIR soil reflectance with partial least squares regression and spectral feature selection techniques , 2011 .

[18]  J. Eitel,et al.  Simultaneous measurements of plant structure and chlorophyll content in broadleaf saplings with a terrestrial laser scanner , 2010 .

[19]  Thomas Cudahy,et al.  Applicability of the Thermal Infrared Spectral Region for the Prediction of Soil Properties Across Semi-Arid Agricultural Landscapes , 2012, Remote. Sens..

[20]  J. Peñuelas,et al.  The reflectance at the 950–970 nm region as an indicator of plant water status , 1993 .

[21]  Christian Nansen,et al.  Use of Variogram Parameters in Analysis of Hyperspectral Imaging Data Acquired from Dual-Stressed Crop Leaves , 2012, Remote. Sens..

[22]  P. Curran Remote sensing of foliar chemistry , 1989 .

[23]  Benoit Rivard,et al.  Comparison of spectral indices obtained using multiple spectroradiometers , 2006 .

[24]  Clement Atzberger,et al.  Comparative analysis of three chemometric techniques for the spectroradiometric assessment of canopy chlorophyll content in winter wheat , 2010 .

[25]  Clement Atzberger,et al.  Retrieval of chlorophyll and nitrogen in Norway spruce (Picea abies L. Karst.) using imaging spectroscopy , 2010, Int. J. Appl. Earth Obs. Geoinformation.

[26]  Ben Somers,et al.  Hyperspectral Reflectance and Fluorescence Imaging to Detect Scab Induced Stress in Apple Leaves , 2009, Remote. Sens..

[27]  J. Hill,et al.  MEASURING WATER AND CHLOROPHYLL CONTENT ON THE LEAF AND CANOPY SCALE , 2011 .

[28]  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.

[29]  S. Wold,et al.  Some recent developments in PLS modeling , 2001 .

[30]  Roberta E. Martin,et al.  PROSPECT-4 and 5: Advances in the leaf optical properties model separating photosynthetic pigments , 2008 .

[31]  Bisun Datt,et al.  Remote Sensing of Water Content in Eucalyptus Leaves , 1999 .

[32]  G. A. Blackburn,et al.  Quantifying Chlorophylls and Caroteniods at Leaf and Canopy Scales: An Evaluation of Some Hyperspectral Approaches , 1998 .

[33]  Stefan Kaiser,et al.  Simulation of Spatial Sensor Characteristics in the Context of the EnMAP Hyperspectral Mission , 2010, IEEE Transactions on Geoscience and Remote Sensing.

[34]  J. Markwell,et al.  Calibration of the Minolta SPAD-502 leaf chlorophyll meter , 2004, Photosynthesis Research.

[35]  B. Yoder,et al.  Predicting nitrogen and chlorophyll content and concentrations from reflectance spectra (400–2500 nm) at leaf and canopy scales , 1995 .

[36]  N. Breda,et al.  Temperate forest trees and stands under severe drought: a review of ecophysiological responses, adaptation processes and long-term consequences , 2006 .

[37]  K. Shadan,et al.  Available online: , 2012 .

[38]  J. Peñuelas,et al.  Estimation of plant water concentration by the reflectance Water Index WI (R900/R970) , 1997 .