MONITORING HEATHLAND HABITAT STATUS ...

MONITORING HEATHLAND HABITAT STATUS USING HYPERSPECTRAL IMAGE CLASSIFICATION AND UNMIXING S. Delalieuxa, B. Somersb, B. Haesta, L. Kooistrac, C.A. Mücherd, J. Vanden Borree a

Centre of Expertise in Remote Sensing and Atmospheric Processes (TAP), the Flemish Institute for Technological Research (VITO), Boeretang 200, 2400 Mol, Belgium, b M3- BIORES, KULeuven, W. de Croylaan 42, 3001 Heverlee, Belgium, c Wageningen University, Laboratory of Geo-Information Science and Remote Sensing, PO Box 47, 6700 AA Wageningen, The Netherlands, dAlterra, Wageningen University and Research Centre, PO Box 47, 6700 AA Wageningen, The Netherlands, e Research Institute for Nature and Forest (INBO), Kliniekstraat 25, 1070 Brussels, Belgium ABSTRACT Natura 2000 has as main objective the achievement or maintenance of a favorable conservation status of habitats protected by the EU Habitats directives. Within this framework, this study examines a strategy to characterize the status of heathland vegetation from airborne hyperspectral AHS data in the Kalmthoutse Heide, Flanders, Belgium. A hierarchical classification scheme was set-up with the highest detail focusing on vegetation structural elements that determine the conservation status of the habitat. Although conventional classification algorithms performed very well (accuracies > 90%) in discriminating broad land cover classes and habitat types (level 1 to 3), they failed in accurately distinguishing different heather age classes which are an important indicator for the structural quality of the heathland habitat (level 4). Since all heather life stages have their specific structural characteristics, a subpixel unmixing approach succeeded by a decision tree classification was implemented to map variations in heathland morphology and as such enhance the ecological value of information derived from remote sensing data. Index Terms— Natura 2000, unmixing, classification, AHS hyperspectral data, habitat status 1. INTRODUCTION Intensification and industrialization of agricultural land use leads to serious losses of biodiversity. This problem has been recognized by environmental policies which has resulted in the liability of European Member States to maintain or restore the natural habitats and species of wild fauna and flora of Community interest [1]. Besides aiming to preserve habitats and species in a coordinated network, another main challenge for Natura 2000 network implementation is the design of accurate, simple and repeatable methods for habitat and species monitoring. Till now, most ecotope mapping tasks are derived from field observations in combination with aerial photo interpretation.

This survey driven approach is labor-intensive and time consuming, and hence not suitable to be done frequently. Consequently, given the extent of the protected areas, remote sensing is often regarded as a valuable, accurate and repetitive tool to aid in the mapping and monitoring of habitat types and their conservation status assessment. Since heathlands are highly valued for a variety of reasons, including their value as cultural landscapes, their historical associations, their characteristic and frequently endangered biodiversity and their value as subjects for ecological study and research, many heathland sites, including the Kalmthoutse Heide in Belgium, were included in the European Natura 2000 program. This study attempts to characterize the conservation status of this heathland using remote sensing. However, conventional remote sensing classification methods still suffer from important limitations for detailed heathland status mapping in several aspects. One major limitation is the degree of detail that can be monitored by conventional remote sensing classification methods. Pixel based classification methods lack the ability of acquiring detailed information, such as the age structure of the heather, an important factor for conservation status decision making. The life cycle of heather (Calluna vulgaris) can be described in terms of a pioneer, building, mature and degeneration stage [2]. The pioneer stage is characterized by small heather cover with other species of vascular plants reaching their greatest abundance. In the building phase, heather excludes all other species. Mature heather is typically less vigorous and the centre of the bush begins to open. Finally, in the degeneration stage which leads to death, the plant canopy continuous to open and exposes more ground or an undergrowing moss layer. In a heathland with a favorable conservation status, all stages of heather should be present in different amounts. Because of these specific age-related structural characteristics, much potential lies in the incorporation of subpixel classification or unmixing in the remote sensing study to enable quality assessment. Previous research [3], however, indicated that

traditional Spectral Mixture Analysis (SMA) failed in assessing the influence of phenology of the heather vegetation. The current study therefore presents a more advanced unmixing approach which includes the inherent spectral variability of age class endmembers to improve the assessment of the spectral differences in heather age classes as a habitat quality indicator at the local level. In first instance, efforts have been put on mapping vegetation species based on a supervised classification technique involving the evaluation of pixels as classification features. Afterwards, this classification framework is extended with the introduction of subpixel fraction estimates. Resulting features, i.e., percentages sand, shadow and heather, can due to the specific structural characteristics of heather life cycle stages, compensate for a lack of detailed structural or phenological information needed to define conservation status. Mature heather canopies, for example, are supposed to be more closed compared to young heather canopies, resulting in less influence of soil in the mixed spectral signature. A decision tree classification of the unmixing results can as such learn more about the structural characteristics of different heather age classes. 2. MATERIALS AND METHODS Airborne Hyperspectral line-Scanner radiometer (AHS-160) images of the Kalmthoutse Heide, Flanders, Belgium (Lat.: 51.41° N, Long.: 04.37° E), were acquired in June 2007. The AHS sensor was mounted on a CASA C-212 airplane operated by INTA, equipped with 63 spectral bands in the visual and near-infrared spectral domain (400 to 2500 nm). Images with a spatial resolution of 2.4 by 2.4m were radiometrically calibrated and accurately geo-referenced. Atmospheric correction was performed using software [4] equivalent to ATCOR4 [5]. The originally acquired AHS images were mosaicked into a seamless data product. The atmospheric influence on reflectance values caused by off-nadir viewing was reduced by using data from the image with the smallest View Zenith Angle (VZA) in overlapping areas. This mosaic approach can be justified because of the very large overlap between neighboring tracks. In order to calibrate the airborne data as well as to assess data quality, in-situ data were acquired during overflight. Following the user requirement analysis, a list of targeted vegetation types was drawn, and representative samples of these types were described in the field (in homogeneous circles of at least 10 m diameter). This list was not only inspired by the habitat types sensu stricto, but also by other land cover classes and by descriptors of habitat quality. 3. CLASSIFICATION A heathland classification scheme was set-up hierarchically subdivided in 4 levels. Level 1 consists of 6 classes representing broad land cover classes: heathland,

grassland, forest, sand dunes, water and arable land. These 6 classes are gradually refined into 27 classes at level 4, inspired not only by the definitions of the habitat types but also by the structures and functions that are crucial for the assessment of habitat quality. Classification was restricted to the nature area of the Kalmthoutse Heide, i.e., excluding the areas specified as agricultural land and built-up area. Also water surfaces were restrained from the classification analyses, resulting in a total amount of 678 samples to be classified till level 4. Supervised classification was performed on each level using Linear Discriminant Analysis (LDA), and a floating search algorithm (SFFS) to select the most relevant features, i.e. bands or wavelet features. Sequential floating forward selection (SFFS) first picks the variable with the best score for a criterion. This criterion can be the result of a classification or some measure for class separation. In this study, the probability of correct classification is calculated on a low dimensional space which is obtained by projecting the evaluated feature subset using linear discriminant analysis [6]. LDA minimizes the ratio of the within class over the between class scatter matrices. After the first variable is selected, a second variable is added for which the combination of both gives the best score for the criterion, and so on. After each forward step, one or more backward steps are taken, i.e., removing a previously selected variable to see if the separability measure can be increased at that level [7]. This feature extraction algorithm is generic, in a sense that it performs well, regardless of the nature and complexity of the application and sensor characteristics (i.e., number and width of spectral bands) [8]. A ten-fold crossvalidation approach was used to determine the classification accuracy. 4. UNMIXING Most current airborne hyperspectral sensors are characterized by a spatial resolution which limits an accurate determination of heather age classes in heathland ecosystems. The sub-pixel contribution of soil and shadow can help to identify the age class of the heather due to the specific structural characteristics associated with each life stage. The basic linear SMA describes a mixed spectrum (r) as a linear combination of pure spectral signatures of its constituent components (i.e., endmembers), weighted by their sub-pixel fractional cover and can be formulated as:

r = Mf + ε with

m

∑f

j

= 1 and 0 ≤ f j ≤ 1

(1)

j =1

In Eq. (1) M is a matrix wherein each column corresponds to the spectral signal of a specific endmember. f is a column vector [f1,…,fm]T denoting the cover fractions occupied by each of the m endmembers in the pixel. ε is the portion of the spectrum that cannot be modelled. The approach in this

study to solve Eq. (1) is the estimation of abundance fractions using least squares error (LSE) estimates [9]: 2

 m   (2) εi ² = M i , j × f j − ri    i =1 i =1  j =1  In Eq. (2) n is the number of available spectral bands. n

∑

∑ ∑(

)

n

Since it is well-known that traditional SMA fails to fully account for the spectral variability associated with spatial and temporal changes in soil and heather, this study will test the potential of Multiple Endmember Spectral Mixture Analysis (MESMA). Roberts et al. [10] proposed this algorithm to decompose spectral mixtures by using numerous endmember combinations in an iterative procedure. MESMA adopts as the best model the one that has a smaller root mean square error when compared to the spectral curve of the pixel [11]. Endmembers were extracted from the AHS imagery from homogeneous plots identified by field observations. Based on the structural differences of different age classes, sand, shadow and Calluna heather were selected as endmembers. Since the shortwave infrared spectral region (i.e., bands 2263) and bands 16 and 17 were dominated by noise, these were excluded from further analysis. Sub-pixel fraction estimates obtained by SMA and MESMA were subsequently used as input parameter in a decision tree classification model to assess the numerical trade-offs in endmember cover fractions between the different age classes. Tree-based classification was performed via the Rpart library [12-13] of the computational language ‘R’ (Version R210, 2010) available as freeware. A ten-fold cross-validation approach was used to determine the classification accuracy and the optimal size of the tree.

5. RESULTS & DISCUSSION 5.1. Classification Table 1 represents the overall accuracy and the kappa values of the LDA based supervised classification at the different hierarchical levels. Table 1: Overall accuracy and kappa index of the supervised LDA classification at different levels Overall accuracy

Kappa Index

Level 1

0.96

0.95

Level 2

0.94

0.93

Level 3

0.90

0.90

Level 4

0.84

0.79

At first sight, these results are promising for all classification levels. Broad land cover classes: heathland, grassland, forest, sand dunes, water and arable land were almost perfectly classified. Also, more detailed habitat types

(level 2 and 3) were classified with high accuracy. From Table 1, it can be decided that also at level 4, good classification accuracies were obtained. However, when looking more careful into the error matrix of the level 4 classification performed on a level 3 classified heathland map, it becomes clear that most of the level 4 classification errors are attributed to a failure in discriminating different heather age classes. The error matrix, overall accuracy and kappa index of the level 4 classification for the most dominant vegetation class, i.e., Calluna vulgaris, is given in Table 2. Heather cover in the pioneer stage is labeled Hdcy. The building and mature were grouped in one age class, Hdca, because the distinction between these groups was not always possible in the field. No degeneration stage was taken into account, since only a very limited amount of ground truth pixels were observed for this class in the Kalmthoutse Heide. Table 2 Error matrix, overall accuracy and kappa index of the supervised LDA classification on Calluna vulgaris (Hdca: aged, Hdcm: mixed, Hdcy: young). CLASS

Hdca

Hdcm

Hdcy

Hdca

68

28

0

Overall accuracy

Kappa Index

Hdcm

34

54

1

0.75

0.63

Hdcy

3

1

80

Since the presence or absence of different developmental stages of Calluna (Hdcm) determines the quality of the heather, it can be concluded from Table 2 that the conventional LDA classification technique is not optimal for detailed conservation status mapping.

5.2. Unmixing To enable a more detailed look into heather quality, subpixel unmixing analysis followed by a decision tree classification was applied on a level 3 classified heather image. Results of the classification of SMA as well as MESMA results are given in Table 3 and 4, respectively. Table 3 Error matrix, overall accuracy and kappa index of classified SMA on Calluna vulgaris (Hdca: aged, Hdcm: mixed, Hdcy: young). SMA

Hdca

Hdcm

Hdcy

82

18

0

Overall accuracy

Kappa Index

Hdcm

9

57

7

0.82

0.73

Hdcy

7

7

82

Hdca

Table 4 Error matrix, overall accuracy and kappa index of classified MESMA on Calluna vulgaris (Hdca: aged, Hdcm: mixed, Hdcy: young). MESMA Hdca Hdcm Hdcy

Hdca 78 17 3

Hdcm 12 62 8

Hdcy 10 7 72

Overall accuracy 0.79

Kappa Index 0.68

As can be concluded from Table 3 and 4, results are slightly better for the tree based classified unmixing approaches compared to the supervised classification method. The overall accuracy of the traditional SMA classification even reaches values above 80%. The lower accuracy of MESMA could probably be attributed to a suboptimal selection of endmembers in this preliminary study. The decision tree shown in Figure 1 represents the outcome of the classified unmixing, pruned back to three groups to allow a better interpretation.

Fig. 1 Classification tree of the unmixing results

The endmembers soil/sand and shadow are selected by the decision tree model to make a distinction between Hdca, Hdcy and Hdcm. Results indicate that heather pixels containing less than 13% sand mainly belong to the ‘aged’ class. At this stage, heather is indeed dense and wellestablished and will cover most of the soil surface. Heather pixels with less than 13% sand are categorized in the young or mixed class. Young heather plants are small and they do not fully cover the underlying soil. The distinction between young and mixed habitats is finally made by the degree of shadow. Young, little heather plants are likely to cause less shadow than mixed pixels and are indeed classified as having ‘less than 31% shadow’. Pixels containing more than 31% shadow are classified as Hdcm. As such, the results obtained from this classification exercise can be explained by natural heather structure.

6. CONCLUSIONS This study illustrates the high potential of remote sensing methods for detailed habitat monitoring in the framework of Natura 2000. Supervised classification techniques enable an accurate mapping of broad habitat classes. A further refinement of the analysis towards habitat quality assessment has been illustrated by using subpixel unmixing techniques. The specific structural characteristics of the

different heather age classes were thereby used to enhance the ecological value of information derived from remote sensing data. These results are, however, preliminary and further research is needed to demonstrate the full potential of techniques such as MESMA by e.g., optimizing the endmember selection procedure.

7. ACKNOWLEDGEMENTS We thank the Belgian Science Policy for funding this research. Also many thanks to all Habistat project partners including, INBO, VUB, UA, and Alterra. 8. REFERENCES [1] Anon, “Habitats directive: council directive 92:43/EEC of 21 May.”, 1992. [2] N.R. Webb, Heathlands, W. Collins & Sons., London, 1986. [3] Kooistra, L., Mücher, C.A., Chan, J.C-W, Vanden Borre, J., Haest, B., “Use of spectral mixture analysis for characterisation of function and structure of heathland habitat types.” Proceedings of the 6th EARSel SIG IS Workshop European Association of Remote Sensing Laboratories Workshop, Tel Aviv University, Tel-Aviv, March 16-19, 2009. [4] Biesemans, J., Sterckx, S., Knaeps, E., Vreys, K., Adriaensen, S., Hooyberghs, J., Meuleman, K., Deronde, B., Everaerts, J., Schläpfer, D., Nieke, J., “Image processing workflows for airborne remote sensing”, Proceedings of the 5th EARSel Workshop on Imaging Spectroscopy, Bruges, Belgium, April 23-25, 2007. [5] R. Richter, “Atmospheric/ Topographic Correction for airborne Imagery.” ATCOR-4 user guide version 3.1., DLR, Wessling, Germany, 2004. [6] Fischer, R.A., “The use of multiple measurements in taxonomic problems”, Annals of Eugenics 7, 179-188, 1936. [7] Pudil, P., Novovievova, J., Kittler, J.,“Floating search methods in feature selection”, Pattern Recognition Letters, 15, 108-119, 1994. [8] Kempeneers, P., De Backer, S., Debruyn, W., Coppin, P., Scheunders, P.,“Generic wavelet-based hyperspectral classification applied to vegetation stress detection.” IEEE, Transactions on Geoscience and Remote Sensing, 43, 610-614, 2005. [9] Barducci, A., and Mecocci, A., “Theoretical and experimental assesment of noise effects on least-squares spectral unmixing of hyperspectral images”, Optical Engineering, 44, 2005. [10] Roberts, D.A., Gardner, M., Church, R., Ustin, S., Scheer, G., and Green, R.O. “Mapping Chaparral in the Santa Monica Mountains Using Multiple Endmember Spectral Mixture Models”, Remote Sensing of Environment, 65, 267-279, 1998. [11] Okin, G.S., Okin, W.J., Roberts, D.A., Murray, B., “Multiple Endmember spectral Mixture Analysis: Application to an Arid/Semi-Arid Landscape.” Summaries of the 7 th JPL Airborne Earth Science Workshop, JPL Publication 97-21, 291-299, 1998. [12] Thernau, T.M., Atkinson, E.J., “An introduction to recursive partitioning using the rpart routines” In: Technical report series, 61. Rochester, MN., 1997. [13] Maindonald, J., Braun, J. “Data analysis and graphics using R: an examplebased approach” In: Tree-based Classification and Regression. Cambridge University Press, Cambridge, pp. 259–280, 2003.