Use of hyperspectral data for deriving vegetation types in Savannahs in Central Namibia

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1 Use of hyperspectral data for deriving vegetation types in Savannahs in Central Namibia Lena Lieckfeld a*, Jens Oldeland b, Bettina Weber, Christoph Schultz c, Andreas Müller a, Michael Schmidt a, Norbert Jürgens b, a German Aerospace Center (DLR), Oberpfaffenhofen, Germany b University of Hamburg, Biozentrum Klein Flottbek, Hamburg, Germany c University of Würzburg, Geographie and Remote Sensing, Würzburg, Germany * Corresponding author contact: lena.lieckfeld@dlr.de

Aerospace Center (DLR), Oberpfaffenhofen, Germany b University of Hamburg, Biozentrum Klein Flottbek, Hamburg,

2 Contents 1. Motivation and Objectives 2. Campaign 3. Methods 4. Next steps 5. Outlook

3 1. Motivation and Objectives How do the different farming strategies affect the semi natural vegetation in Savannah ecosystems? To what extent can vegetation types be differentiated using CHRIS/PROBA? Which classification methods are suitable for CHRIS/PROBA? How can botanical field data be combined with Remote Sensing?

To what extent can vegetation types be differentiated using CHRIS/PROBA?

4 Omatako 2. Campaign Rehoboth

5 2. Campaign Omatako: Thorn bush savannah Rehoboth: Dwarf shrub savannah Altitude: 1510 m Precipitation: 350mm (2006: 600mm) Altitude: 1600 m Precipitation: 250mm (2006: 400mm)

6 2. Campaign The Campaign took place in March/April 2006 during the rainy season (vegetation maximum) We collected field spectra Vegetation plot data At the same time a CHRIS/PROBA overflight was conducted HyMap data from 2004 and 2005

field spectra Vegetation plot data At the same time a

7 2. Campaign CHRIS/PROBA Mode 5, March 2006 CHRIS/PROBA Mode 1, March 2006

8 Vegetation plot data: 2. Campaign On 25x25m Species composition Cover Value Structure

9 2. Campaign ASD-Measurements: FieldSpec FR spectroradiometer Spectral range from nm Spectral resolution 1 nm FOV 25 Only the dominant vegetation was measured

10 2. Campaign Field spectra: preprocessing Spectralon correction Jump correction Calculation of the mean-spectrum Resampling to CHRIS/PROBA

11 3. Methods Preprocessing steps of CHRIS/PROBA: geo-correction before destriping CHRIS/PROBA atmospheric correction destriping after destriping CHRIS/PROBA

12 3. Methods Difficulties: Possiblities to destinguish between the desired classes?

13 3. Methods Combining botanical field data with Remote Sensing via Partial Least Square regession Vegetation plots Matrix Y CHRIS/PROBA reflectance values Matrix X PLS Map of Vegetation Types

14 3. Methods What is PLS and when is it used? Regression technique which handles many, highly collinear independent variables Goal is to predict Y (Vegetation data) from X (refelctance values) Iterative PCA-Algorithm which maximises prediction accuracy by linking Y and X using covariance matrix Criterion: covarianz = max

Goal is to predict Y (Vegetation data) from X (refelctance values) Iterative

15 3. Methods Building up a Set of Variables Sample Band 1 Band 2 Band Band N = Vegetation Plot X-Matrix

16 3. Methods Vegetation Field Data (2005 / 2006 / 2007) Phytosociological Multivariate Statistic DCA - SITES Cluster Dendrogram T4-1 T4-3 T1-5 T2-1 T1-8 T1-6 T2-8 T5-4 T1-7 T5-3 T5-2 T3-8 T5-1 T2-3 T4-4 T5-7 T3-6 T4-5 T3-1 T1-1 T3-7 T5-5 T5-6 T1-3 T1-4 T1-2 T4-7 T2-2 T2-6 T2-4 T3-4 T2-7 T3-5 T4-8 T2-5 T5-8 T3-2 T T4-2 T3-3 DCA Height Z DCA1 dist.euclid hclust (*, "complete") NMDS Cluster Sample Group A Group B Group C Y-Matrix

T2-4 T3-4 T2-7 T3-5 T4-8 T2-5 T5-8 T3-2 T4-6 0 5 10 15 20 T4-2 T3-3 DCA2-1.0-0.5 0.0 0.5 1.0 1.5 Height Z6-1.0-0.5 0.0 0.5 1.0 1.5 DCA1 dist.

17 3. Methods ISODATA & Partial Least Square (PLS) Regression for classification of vegetation types in Central Namibia Iso-Data Classification with 4 classes Classified-Image for two classes with PLS Degraded Grassland Dwarf Shrub

Central Namibia Iso-Data Classification with 4 classes

18 4. Next steps Different ways for enhancing the classification Vegetation Plots Matrix Y CHRIS/PROBA Reflectance values Matrix X more vegetation samples PLS Selected VI s environmental variables as classifier improving statistical techniques Map of Vegetation Types derivatives as input bands preclassified image

samples PLS Selected VI s environmental variables as classifier improving

19 5. Outlook The PLS is a promising approach to combine botanic data with CHRIS/PROBA The resulting vegetation type map will be an ecological basis for any measurements regarding sustainability of the different farming strategies

type map will be an ecological basis for any

20 Thank you for your attention

21 1. Backround Helmholtz Research Network: Helmholtz EOS PhD-Program Teamwork between two fields Botanic and Remote Sensing BIOTA AFRICA An international Research Network on Biodiversity, sustainable use and conservation

22 4. Conclusion A Map of the different vegetation types an ecological basis for any measurements regarding sustainability of the different farming strategies A methodology or a tool for detecting differences between vegetation spectra which could be used for analysing field and image spectra

23 3. Methods Generalized Partial Least Squares gpls Ding, Beiying & Gentleman, Robert; Classification Using Generalized Partial Least; Squares; 2004; Bioconductor Project; Bioconductor Project Working Papers; Abdi, Hervé, 2007, Partial Least Square Regression PLS-Regression; Encyclopedia of Measuremnet and Statistics; Neil Salkind (Ed.); Thousand Oaks (CA):Sage

24 2. Campaign Use of hyperspectral data for deriving vegetation types in Savannahs in Central Namibia The precondition to achieve this goal is of course that the vegetation types are indeed spectrally distinguishable in-situ measurements were taken using a spectroradiometer to check if this precondition is fullfiled

CROP CLASSIFICATION WITH HYPERSPECTRAL DATA OF THE HYMAP SENSOR USING DIFFERENT FEATURE EXTRACTION TECHNIQUES

Proceedings of the 2 nd Workshop of the EARSeL SIG on Land Use and Land Cover CROP CLASSIFICATION WITH HYPERSPECTRAL DATA OF THE HYMAP SENSOR USING DIFFERENT FEATURE EXTRACTION TECHNIQUES Sebastian Mader