Using VNIR and SWIR field imaging spectroscopy for drought stress monitoring of beech seedlings

Drought stress is expected to become a recurrent problem for central European forests due to regional climate change. In order to study the effects on one of the most common tree species in Germany, the European beech (Fagus sylvatica), young potted beech trees were exposed to drought stress in a controlled experiment and their reaction was observed using visible/near-infrared (VNIR) and shortwave infrared (SWIR) field imaging spectroscopy cameras mounted on a platform. Equivalent water thickness (EWT) and leaf chlorophyll content (LCC) were measured and partial least squares (PLS) regression models were trained using these reference measurements and reflectance spectra of the trees. The models were applied to create maps of these properties with a spatial resolution in the millimetre range. These maps can be used to study the spatial distribution of EWT and LCC for single leaves or even for intra-leaf variability. Both properties can be estimated using only the VNIR sensor, but EWT estimation improves considerably by also incorporating SWIR data. LCC estimations with SWIR data alone do not work satisfactorily.

[1]  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 .

[2]  Joachim Hill,et al.  Modeling and Mapping of Soil Salinity with Reflectance Spectroscopy and Landsat Data Using Two Quantitative Methods (PLSR and MARS) , 2014, Remote. Sens..

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

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

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

[6]  S. Schiavon,et al.  Climate Change 2007: Impacts, Adaptation and Vulnerability. , 2007 .

[7]  A. Gitelson,et al.  Remote estimation of chlorophyll content in higher plant leaves , 1997 .

[8]  M. Fortin,et al.  The Impact of Windstorm Damage in the Assessment of the Carbon Balance in Even-Aged Fagus sylvatica L. Stands , 2014 .

[9]  Jens Nieke,et al.  APEX - the Hyperspectral ESA Airborne Prism Experiment , 2008, Sensors.

[10]  Emanuela Lupo,et al.  Hyperspectral Proximal Sensing of Salix Alba Trees in the Sacco River Valley (Latium, Italy) , 2013, Sensors.

[11]  Michael Vohland,et al.  Determination of soil properties with visible to near- and mid-infrared spectroscopy: Effects of spectral variable selection , 2014 .

[12]  Joachim Hill,et al.  Fusion of full-waveform lidar and imaging spectroscopy remote sensing data for the characterization of forest stands , 2013 .

[13]  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.

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

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

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

[17]  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.

[18]  Joachim Hill,et al.  Field Imaging Spectroscopy of Beech Seedlings under Dryness Stress , 2012, Remote. Sens..

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

[20]  R. Kokaly,et al.  Characterizing canopy biochemistry from imaging spectroscopy and its application to ecosystem studies , 2009 .

[21]  Henning Buddenbaum,et al.  The Effects of Spectral Pretreatments on Chemometric Analyses of Soil Profiles Using Laboratory Imaging Spectroscopy , 2012 .

[22]  T. Wilbanks,et al.  Contribution of Working Group II to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change , 2007 .

[23]  Henning Buddenbaum,et al.  Comparison of Feature Reduction Algorithms for Classifying Tree Species With Hyperspectral Data on Three Central European Test Sites , 2014, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[24]  A F Goetz,et al.  Imaging Spectrometry for Earth Remote Sensing , 1985, Science.

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

[26]  G. A. Mahdiraji,et al.  Airborne hyperspectral discrimination of tree species with different ages using discrete wavelet transform , 2015 .

[27]  M. Steffens.  Laboratory imaging spectroscopy of a stagnic Luvisol profile - high resolution soil characterisation , 2013 .

[28]  Philippe Lagueux,et al.  A Hyperspectral Thermal Infrared Imaging Instrument for Natural Resources Applications , 2012, Remote. Sens..

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

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

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

[32]  Florian Hartig,et al.  Stratified aboveground forest biomass estimation by remote sensing data , 2015, Int. J. Appl. Earth Obs. Geoinformation.

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

[34]  B. Datt,et al.  Visible/near infrared reflectance and chlorophyll content in Eucalyptus leaves , 1999 .

[35]  H. White,et al.  Reflectance processing of remote sensing spectroradiometer data , 2001 .

[36]  Roberta E. Martin,et al.  Carnegie Airborne Observatory-2: Increasing science data dimensionality via high-fidelity multi-sensor fusion , 2012 .

[37]  W. Verhoef,et al.  PROSPECT+SAIL models: A review of use for vegetation characterization , 2009 .

[38]  Riccardo Leardi,et al.  Application of genetic algorithm–PLS for feature selection in spectral data sets , 2000 .

[39]  Patrick Hostert,et al.  Science Plan of the Environmental Mapping and Analysis Program (EnMAP) , 2012 .

[40]  Henning Buddenbaum,et al.  Abbildende und nichtabbildende Geläindespektrometrie zur Untersuchung von Stressphänomenen an Buchenpflanzen The use of imaging and non-imaging Spectroscopy for the determination of stress phenomena of beech trees , 2014 .

[41]  Flor Álvarez-Taboada,et al.  Spectroscopic Determination of Aboveground Biomass in Grasslands Using Spectral Transformations, Support Vector Machine and Partial Least Squares Regression , 2013, Sensors.

[42]  D. Klein,et al.  The Contribution of Managed and Unmanaged Forests to Climate Change Mitigation—A Model Approach at Stand Level for the Main Tree Species in Bavaria , 2013 .