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April 29th,2005 Warsaw University

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"Seasonal variability in spectral reflectance of grasslands along a dry-mesic gradient in Switzerland"Achilleas Psomas1,2, Niklaus E. Zimmermann1, Mathias Kneubühler2, Tobias Kellenberger2, Klaus Itten21.Swiss Federal Research Institute WSL,2. Remote Sensing Laboratories (RSL), University of Zurich

April 29th,2005

Warsaw University


  • Introduction

  • Objectives

  • Data Processing-Statistical analysis

  • Initial Results

  • Discussion


  • Dry meadows and pastures in Switzerland are species-rich habitats resulting from a traditional agricultural land use.

  • 40% of plant and over 50% of animal species present on dry meadows are classified as endangered

  • 90% of dry grasslands have been transformed to other land cover types

  • TWW Project "Dry Grassland in Switzerland"(Trockenwiesen und –weiden,1995)

  • Creation of a federal inventory so ecologically valuable grasslands could be given an increased protection by law.

General Objective

  • To develop, apply, and test different methods based on remote sensing datasets and techniques for identification and monitoring of dry meadows and pastures in Switzerland

  • Main project parts:

    Part A:Field Spectrometry-(Plot to Field)

    Part B:Imaging Spectrometry-(Field to Region)

    Part C:Multitemporal Landsat TM approach-(Region to


General Objective

Objectives-Field Spectrometry

  • Examine the potential of using the seasonal variability in spectral

    reflectance for discriminating dry meadows and pastures.

  • Identify the best spectral wavelengths to discriminating grasslands

    of different type. Which are the spectral wavelengths with statistical

    significant differences?

  • Identify the optimal time or times during the growing season

    for discriminating and classifying different types of grasslands.

Example of grasslands and pastures

Dry [MB]

Semi-dry [AEMB]

Preprocessing-Statistical analysis

Structure of dataset

Collection-Temporal resolution

  • Field spectroradiometer, Analytical Spectral Devices FieldSpec Pro

  • 4grassland types examined along a dry-mesic gradient

  • 12 samplefields at Aargau and Chur

  • 12 repeats (time steps) between


  • 20.000 spectral signatures collected

Data preparation and statistical analyses

  • Removal of errors mentioned at the field protocol.

  • Identification of potentially false recordings. Changing weather-moisture conditions. Unforced errors.

  • Normalization of data : Continuum Removal.

  • Mann-Whitney U Test (Wilcox test)

  • Classification and Regression Tree Analysis (C&RT) on statistically significant wavelength for selection of wavelengths.

  • Feature space distance analysis

Identification of potential errors

Continuum Removal I

  • It standardizes reflectance spectra to allow comparison of absorption features.

  • Spectral absorption-depth method for identifying chlorophyll, water, cellulose, lignin image spectral features

  • Minimization of factors like atmospheric absorption, soil exposure, other absorbers in the leaf (Kruse et al. 1985; Clark et al. 1987; Kruse et al. 1993a).

  • A continuum is formed by fitting straight line segments between the maxima of the spectral curve

Continuum Removal I

  • It standardizes reflectance spectra to allow comparison of absorption features.

Continuum Removal II

Continuum Removal III

Statistical Analysis I

  • Statistical significance of spectral response was tested with the Mann-Whitney U Test (Wilcox test) for a p<0.01 for each wavelength of each field per for recording day.

  • Analysis was done between individual fields and between each grassland type. (for every individual day)

  • Continuum removed spectra and the original recordings were tested.

  • Classification and Regression Tree Analysis (C&RT) on statistically significant wavelength for selection of wavelengths.

  • Repeated (15x) 10-fold cross validation to optimize the pruning of the tree

  • Feature space analysis using the Jeffries-Matusita distance.

Statistical Analysis II

Classification and Regression Trees (C&RT)

  • Results presented on a tree are easily summarized and interpreted.

  • Flexible in handling different response data types and a big number of explanatory variables.

  • Ease and robustness of construction.

  • Tree methods are nonparametric and nonlinear

Statistical Analysis III





350nm x 100

351nm x 100



2500nm x 100


350nm x 120

351nm x 120



2500nm x 120






Wilcox test

Wilcox test

  • For every day all possible field combination are checked for statistical significance per wavelength.

  • E.g.: Recording day with 6 fields (AE,AEMB1,AEMB2,MB1,MB2,MB3)

    Possible combinations : 15

    Significance tests: 15 combinations x 2000 Wavelengths (variables)

Statistical Analysis IV

Preliminary results


  • 3 Types

    • AE: Mesic, nutrient-rich grassland

    • AEMB: Less Mesic, species-rich grassland

    • MB: Semi-dry, species-rich grassland

  • Aarau

  • 9 time steps

    25. Mai10. Jun25. Jun21. Jul

    28. Jul15. Aug23. Aug02. Sep18. Sep

Significant Wavelengths I

Significant Wavelengths II


 -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- 

Mesic Dry

C&RT Analysis I

C&RT for Original spectral recordings - 10th June 2004

Classification tree:Variables actually used in tree construction:b658 b690 b1608 b505 b705 b551 b1441Number of terminal nodes: 8 Misclassification error rate: 0.07732 = 45 / 582

C&RT Analysis II: Misclassification error rate

C&RT Analysis III: Selected Wavelengths

Feature space distance

  • Jeffries-Matusita Distance


 -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- 

Mesic Dry


  • Increased spectral resolution of hyperspectral recordings provide great opportunities for discriminating grassland types.

  • Recordings during the growing seasongive a better understanding of the spectral differences between grassland types and increase the possibilities for successful discrimination and classification.

  • Continuum removed spectra gave a smaller number of significant wavelengths but overall better class-separability throughout the season.

  • C&RT proved to be a powerful statistical approach for reducing the dimensionality of hyperspectral data and for optimizing the selection of wavelengths that maximized the class separability .

  • Processing of the data, statistical analysis and C&RT analysis was all done in the statistical packageR, making it easily reproducible and adjustable.

Thank you for your attention…

Feature space distance

  • Bhattacharyya Distance




Preliminary results

Preliminary results

Preliminary results

Spectral Reflectance - I

  • The total amount of radiation that strikes an object is referred to as the incident radiation

    incident radiation = reflected radiation + absorbed radiation + transmitted radiation


Continuum Removal II


Preliminary results

Preliminary results



Continuum Removal I

  • Trees can be used for interactive exploration and for description and prediction of patterns and processes. Advantages of trees include:

  • (1) the flexibility to handle a broad range of response types, including numeric, categorical, ratings, and survival data; (2) invariance to monotonic transformations of the explanatory variables;

  • (3) ease and robustness of construction;

  • (4) ease of interpretation;

  • (5) the ability to handle missing values in both response and explanatory variables.

  • Thus, trees complement or represent an alternative to many traditional statistical techniques, including multiple regression, analysis of variance, logistic regression, log-linear models, linear discriminant analysis, and survival models.

Discussion-Further steps

  • Separability analysis: Euclidean ,Jeffries-Matusita, Bhattacharyya distance

  • Perform CART tree analysis using the statistically significant spectral bands.

  • Upscaling the results of the analysis to HyMap sensor .(5m spatial resolution,128bands spectral resolution).

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