April 29th,2005 Warsaw University

1. "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

2. Overview • Introduction • Objectives • Data Processing-Statistical analysis • Initial Results • Discussion

3. 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.

4. 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)

5. 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.

6. Example of grasslands and pastures Dry [MB] Semi-dry [AEMB]

7. Preprocessing-Statistical analysis

8. 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 March-October • 20.000 spectral signatures collected

9. 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

10. Identification of potential errors

11. 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

12. Continuum Removal I • It standardizes reflectance spectra to allow comparison of absorption features.

13. Continuum Removal II

14. Continuum Removal III

15. 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.

16. 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

17. Statistical Analysis III 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)

18. Statistical Analysis IV

19. 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

20. Significant Wavelengths I

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

22. 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

23. C&RT Analysis II: Misclassification error rate

24. C&RT Analysis III: Selected Wavelengths

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

26. Discussion • 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.

27. Thank you for your attention…

28. Feature space distance • Bhattacharyya Distance

29. 25-5-2004 MB2 AEMB2

30. Preliminary results

31. Preliminary results

32. Preliminary results

33. 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

34. Scaling-I

35. Continuum Removal II

36. Scaling-II

37. Preliminary results

38. Preliminary results

39. Additional

40. Additional

41. 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.

42. 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).