"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 Itten2 1.Swiss Federal Research Institute WSL, 2. Remote Sensing Laboratories (RSL), University of Zurich April 29th,2005 Warsaw University

Overview Introduction Objectives Data Processing-Statistical analysis Initial Results Discussion

Introduction 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 Landscape)

Field Spectrometry Objectives 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 Semi-dry [AEMB] Dry [MB]

Preprocessing-Statistical analysis

Structure of dataset Collection-Temporal resolution Field spectroradiometer, Analytical Spectral Devices FieldSpec Pro 4 grassland types examined along a dry-mesic gradient 12 sample fields at Aargau and Chur 12 time steps March-October 20.000 spectral signatures collected

Processing of data 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 Continuum removal 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 III

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) Both the 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 distance analysis

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 II AEMB MB p-value Wavelengths 350nm x 100 351nm x 100 .. 2500nm x 100 Wavelengths 350nm x 120 351nm x 120 .. 2500nm x 120 0.002 0.038 .. 0.0004 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 II

Preliminary results Details 3 Types AE: Mesic, nutrient-rich grassland AEMB: Less Mesic, species-rich grassland MB: Semi-dry, species-rich grassland Aarau 9 time steps 25. Mai 10. Jun 25. Jun 21. Jul 28. Jul 15. Aug 23. Aug 02. Sep 18. Sep

Significant Wavelengths I

Significant Wavelengths II AE AEMB MB  -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- --  Mesic Dry

C&RT Analysis I C&RT for Original spectral recordings - 10th June 2004 Classification tree: Variables actually used in tree construction: [1] "b658" "b690" "b1608" "b505" "b705" "b551" "b1441" Number of terminal nodes: 8 Residual mean deviance: 0.4483 = 257.3 / 574 Misclassification error rate: 0.07732 = 45 / 582 C&RT for Original spectral recordings - 10th June 2004

C&RT Analysis II: Misclassification error rate

C&RT Analysis III: Selected Wavelengths

 -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- --  Feature space distance Jeffries-Matusita Distance AE AEMB MB  -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- --  Mesic Dry

Discussion Increased spectral resolution of hyperspectral recordings provide big opportunities for discriminating grassland types. Multitemporal recordings give a better understanding of the spectral differences between grassland types during the growing season and increase the possibilities for a successful discrimination. Continuum removed spectra gave a smaller number of significant wavelengths but overall better class separability results during the season. C&RT enabled the dimension reduction of the hyperspectral data Processing of the data, statistical analysis and C&RT analysis was done totally in R, easily reproducible and adjustable.

Thank you for your attention…

Feature space distance Bhattacharyya Distance

25-5-2004 AEMB2 MB2

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

Spectral Reflectance - II Spectral reflectance is the portion of incident radiation that is reflected by a non-transparent surface

Spectral Reflectance - III Spectral Response of Vegetation

Field Spectrometry II

Scaling-I Part Sensor Spatial Resolution Spectral Resolution Spatial Coverage Altitude A) Field Spectrometry ASD Field Spectroradiometer 0.5m 2150 bands 6-8 fields/day 1.5m B) Imaging Spectrometry HyMap 5m 128 bands 12km x 4km 3km C) Multitemporal Landsat TM Landsat TM 30m 7 bands 180km x 180km 700km

Continuum Removal II

Scaling-II

Preliminary results

Preliminary results

Additional

Additional

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).