Swiss Federal Research Institute WSL "Seasonal variability in spectral reflectance of grasslands along a dry-mesic gradient in Switzerland" Achilleas Psomas 1,2, Niklaus E. Zimmermann 1, Mathias Kneubühler 2, Tobias Kellenberger 2, Klaus Itten 2 1.Swiss Federal Research Institute WSL, 2. Remote Sensing Laboratories (RSL), University of Zurich April 29th,2005 Warsaw University

Swiss Federal Research Institute WSL Overview Introduction Objectives Data Processing-Statistical analysis Initial Results Discussion

Swiss Federal Research Institute WSL 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.

Swiss Federal Research Institute WSL 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.

Swiss Federal Research Institute WSL Example of grasslands and pastures Semi-dry [AEMB] Dry [MB]

Swiss Federal Research Institute WSL Data processing-Statistical analysis

Swiss Federal Research Institute WSL 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 repeats (time steps) between March-October spectral signatures collected Structure of dataset

Swiss Federal Research Institute WSL Data processing Removal of errors mentioned at the field protocol. Identification of potentially false recordings. Changing weather-moisture conditions. Unforced errors. Normalization of data : Continuum Removal.

Swiss Federal Research Institute WSL Identification of potential errors

Swiss Federal Research Institute WSL Continuum Removal I It standardizes reflectance spectra to allow comparison of absorption features.

Swiss Federal Research Institute WSL Continuum Removal II

Swiss Federal Research Institute WSL 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.

Swiss Federal Research Institute WSL Statistical Analysis III AEMB MB Wavelengths 350nm x nm x nm x 100 Wavelengths 350nm x nm x nm x 100 Wavelengths 350nm x nm x nm x 120 Wavelengths 350nm x nm x nm x 120 p-value 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) Wilcox test

Swiss Federal Research Institute WSL Statistical Analysis IV

Swiss Federal Research Institute WSL 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. Mai10. Jun25. Jun21. Jul 28. Jul15. Aug23. Aug02. Sep18. Sep

Swiss Federal Research Institute WSL Significant Wavelengths I

Swiss Federal Research Institute WSL Significant Wavelengths II AE AEMB MB   Mesic Dry

Swiss Federal Research Institute WSL C&RT Analysis I Classification tree: Variables actually used in tree construction: b658 b690 b1608 b505 b705 b551 b1441 Number of terminal nodes: 8 Misclassification error rate: = 45 / 582 C&RT for Original spectral recordings - 10th June 2004

Swiss Federal Research Institute WSL C&RT Analysis II: Misclassification error rate

Swiss Federal Research Institute WSL C&RT Analysis III: Selected Wavelengths

Swiss Federal Research Institute WSL Feature space distance Jeffries-Matusita Distance AE AEMB MB   Mesic Dry

Swiss Federal Research Institute WSL Discussion Increased spectral resolution of hyperspectral recordings provide great opportunities for discriminating grassland types. Recordings during the growing season give 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 optimizing the selection of wavelengths that maximized the class separability. Processing of the data, statistical analysis,C&RT analysis and continuum removal was all done with code using the statistical package R, making it easily reproducible and adjustable.

Swiss Federal Research Institute WSL Thank you for your attention…

Swiss Federal Research Institute WSL Feature space distance Bhattacharyya Distance

Swiss Federal Research Institute WSL AEMB2 MB2

Swiss Federal Research Institute WSL Preliminary results

Swiss Federal Research Institute WSL Preliminary results

Swiss Federal Research Institute WSL Preliminary results

Swiss Federal Research Institute WSL 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

Swiss Federal Research Institute WSL Scaling-I PartSensor Spatial Resolution Spectral Resolution Spatial CoverageAltitude A) Field Spectrometry ASD Field Spectroradiometer 0.5m2150 bands6-8 fields/day1.5m B) Imaging Spectrometry HyMap5m128 bands12km x 4km3km C) Multitemporal Landsat TM Landsat TM30m7 bands180km x 180km700km

Swiss Federal Research Institute WSL Continuum Removal II

Swiss Federal Research Institute WSL Scaling-II

Swiss Federal Research Institute WSL Preliminary results

Swiss Federal Research Institute WSL Preliminary results

Swiss Federal Research Institute WSL Additional

Swiss Federal Research Institute WSL Additional

Swiss Federal Research Institute WSL 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.

Swiss Federal Research Institute WSL 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).

Swiss Federal Research Institute WSL 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)

Swiss Federal Research Institute WSL 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

Swiss Federal Research Institute WSL Continuum Removal II

Swiss Federal Research Institute WSL 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