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MAPPING DYNAMIC CHANGE IN PHUMDI DISTRIBUTION FROM MULTI-TEMPORAL SATELLITE IMAGERY: AN ASSESSMENT ON LOKTAK

FRESHWATER WETLAND, MANIPUR, INDIA

Ngangbam Romeji1 and Oinam Bakimchandra2

1 Research Scholar, Water Resource Development and Management, Indian Institute of Technology Roorkee, Roorkee- 247667, Uttaranchal, India ; E-mail: ngromezi@gmail.com,Tel: +91-1332-285781, Fax: +91-1332-271073 2 Doctoral student (ENWAT), Institut fr Wasserbau der Universitaet Stuttgart, Pfaffenwaldring 61,D-70550 Stuttgart (Vaihingen),Germany ; E-mail: bakimchandra.oinam@iws.uni-stuttgart.de ,Tel: +49-(0)711-685- 69108 (office)

KEYWORDS: Phumdi, wetland, vegetation, spectral, sub-pixel, multi-temporal data, transformation, SAM. ABSTRACT:

Loktak lake, the largest natural freshwater lake or wetland in the north-eastern region of the India, has the characteristic feature of floating heterogeneous masses of vegetation, soil and organic matter known as phumdis. Phumdis have been described to harbour a governing role in the hydrodynamic regime of the Loktak lake basin and socio-economic livelihood over the major influence on biodiversity. As Loktak wetland is covered with as many as 132 plant species in a mixed environment, the use of ground survey methods for phumdis mapping and regular updating is not pragmatic. A combined use of IRS temporal imageries and mapping techniques by extraction of information at sub-pixel level to disseminate boundaries between two or more pixel in close association is framed on the multispectral characteristics of the sensor in segregating the floating phumdis. This technique is aimed at estimating the proportions of specific vegetation classes in Loktak wetland that occur within each pixel and further classify only the phumdis class. The approach adopted in the present study will help to estimate the proportion of the heterogeneous floating vegetation matter in the phumdis present in single pixels and to assign confidences to the estimated proportions. The spectral processing flow leverages the unique spectrally over-determined nature of the images and provides mapped results based on the spatial location of each of the selected endmember spectra. The results are validated with a higher resolution IKONOS data.

INTRODUCTION

1.1 Overview

Loktak lake, the largest natural freshwater lake or wetland in the north-eastern region of India, has the unique feature of floating islands (locally termed as phumdis) surfacing almost two-thirds of the lake spread besides inheriting rich biodiversity. Loktak lake (locally called Loktak pat) represents 61% of the total identified

wetlands in Manipur state and has been designated as a Wetland of International Importance under the Ramsar convention in 1990. The large extent of phumdi proliferation over an area of 116.4 sq. Km in 1989 to 134.6 sq.km in 2002 has been a major concern of the declining biodiversity of the lake. Phumdis play an important role in governing ecological processes and balance of the lake ecosystem. They also influence hydrological regime and

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biodiversity while supporting productive fishery and socio-economic conditions of the people. The use of ground survey methods for phumdis mapping and regular updating of information is not a pragmatic approach as local training datasets do not provide best representation of adjacent classes and become too coarse for an entire image; besides site accessibility difficulties and economic considerations. Its extensive and highly dynamic hydro-eco regime along with the characteristic phumdis, makes conventional hard classification techniques difficult to ascribe the best representation of the manifold classes prevailing in the wetland system. These characteristics make the Loktak and phumdis entity suitable objects for study using satellite images with coarse spatial but high temporal resolution, such as IRS-LISS data.Sub-pixel mapping provides a robust procedure in estimating the proportions of specific classes that occur within each pixel.

1.2 Study Area and Description of Phumdis The study area selected, Loktak lake (locally called Loktak Pat), is a wetland located in the near-central, oval-shaped valley of Manipur state in the north-eastern frontier of India. Loktak lake (located between latitude 93 46' and 93 55' E and longitude 24 25' to 24 42'N) has a lake spread of 286 km2 at a dictated level of 768.50 above msl. The lake merges with other neighbouring wetlands (Utra pat, Sana pat, Tharo pat, Laphu pat, Kumbi pat, etc.) during the flood season to become a larger entity, Loktak pat. The lake margins are intricate to define as it is surrounded by shallow water stagnating over marshes or swamps as well as an array of aquaculture bunds carved out by local villagers, around most of its periphery. The depth of the lake varies intermittently from 0.5m to 4.6m. The other notable feature is the presence of island-like hillocks (14 are

prominent) rising steeply from the Palaeo lake margin. The southern portion of the lake serves as the the only floating wildlife sanctuary in the globe. The largest single mass of phumdis here occupying an area of 40 sq km constitutes the Keibul Lamjao National Park (KLNP). Loktak lake has a unique biosphere whether in circumscribing it as a lake or as a wetland. The term Loktak wetland is use to describe the combined wetland entity, while Loktak lake is referred to the central portion (fig. 1b). Both the nomenclatures are used without strict adherence to standard definitions.

Phumdis are island-like floating heterogeneous biomass of soil, vegetation and organic matter occurring in various sizes and density across Loktak wetland. Such feature has been reported to be similar to the sigimoor in the U.K. Free-floating plants, such as water hyacinth and partly decomposed roots and rhizomes contribute greatly to phumdis expansion. As phumdis are composted annually during January to March for fish-cum-paddy farms by the locale besides its rapid natural decay and growth cycle, the highly dynamic state needs a resolute manifestation of recognition procedure. Phumdis of varying thickness scatter throughout Loktak wetland and a general trend is observed in their distribution. They can be broadly categorized into four classes according to thickness: (i) 0.2 to 0.3 m (ii) 0.3 to 0.5m (iii) 0.5 to 1m and (iv) above 1m. The Central Zone is mainly covered by phumdis which are thick on the edges but almost hollow inside and are mixed with various plant species. These phumdis are mostly athaphums which are specially reared for aquaculture. Phumdis grow vertically adding up layers of decomposed and semi decomposed mass of plant material along with silt and other plant fragments. These distinctive characteristics and aggravating

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aimed at estimating the proportions of specific vegetation classes in Loktak wetland that occur within each pixel and further classify only the phumdis class. The disadvantage here is that some information is lost much in the same way as happens with hard classifiers. An alternative approach (Atkinson, 1997) which consists of converting raster data to vector data by threading the vector boundaries through the original image pixels (instead of between pixels, as standard raster-to-vector conversion algorithms would do) is adopted. This process is called sub-pixel mapping. The key problem here again is determining where the relative proportions of each class are most likely to occur. Although neural networks and fuzzy convolution techniques considers the non-linearity between the elements that compose the spectral signature of a pixel, it is not taken into study as the approach requires extensive training sample and lucid understanding of its internal functions with respect to the prevailing site conditions. A combined use of temporal imageries and classification techniques by extraction of information at sub-pixel level to disseminate boundaries between two or more pixel in close association is framed on the multispectral characteristics of the IRS-LISS sensor in segregating the floating phumdis from the manifold vegetation classes in Loktak pat. Multi-temporal dataset is used to normalize the spectral dimensions of the image data with the spectral response of each ground feature component in a pixel mixture. The objectives of the study are: (a) dissemination of the Loktak wetland vegetation cover, (b) assigning endmembers (the reflectance of pixel component) for the prominent vegetation classes and derive a pure spectra endmember for the plant species falling under phumdis class, (c) arrive at various soft classified maps of

Loktak pat (as SAM, Linear Spectral Unmixing and MTMF) for each temporal period and make a comparative assay of the techniques, (d) examine change detection anomalies for the derived classified maps. However to reach these objectives, it is first necessary to quantify and assign value to each temporal dataset with a deep understanding of the attributes and functions of Loktak wetland especially because these are sometimes of a fast changing nature.

METHODOLOGY

Sub-pixel analysis methods are used to calculate the quantity of target materials in each pixel of an image. The target materials are the respective vegetation for the phumdis and non-phumdis class. Sub-pixel analysis can detect quantities of a target that are much smaller than the pixel size (for IRS LISS-3, 23.5m x 23.5 m) itself. In addition, a whole-pixel method is supplemented in this study to determine whether one or more target materials are abundant within each pixel in the IRS multispectral image on the basis of the spectral similarity between the pixel and target spectra. The Spectral Angle Mapper is one such tool used to compute a spectral angle between each pixel spectrum and each target spectrum. 2.1 Sub-pixel Classification The basic hypothesis in this classification methodology is that the image spectra are the result of mixtures of surface materials, shade and clouds, and that each of these components is linearly independent of the other. Spectral response of each image pixel in every spectral band can be considered as a linear combination of the response of each component (or endmember) present in the mixture. Hence the spectral reflectance ri, for every image pixel in any band i, can be expressed as,

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, , , (1)

where, is an error term, f is fraction of an endmember in a pixel, n is possible number of endmembers in the scene, a is the pure (or characteristic) spectra from the respective endmember. Replacing classes 1 to m with j, then equation (1) can be simplified as,

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To address the problem of where the number of pixels will normally largely exceed the number of endmembers is by using a multi-temporal data set, where the maximum number of endmembers is made equal to the number of spectral bands multiplied by the number of dates for which images are available. Using a more general notation, the set of equations can be more conveniently represented as a matrix, (3) In general with similar order to the above eqn (4) for n pixels and bands, y is a (n * 1) vector of observations, X is a (n * m) matrix of the levels of the independent variables, is a (m * 1) vector of the regression coefficients and is a (n * 1) vector of random errors.

In the study, the Spectral Analyst program in ENVITM (Environment for Visualizing Images Research Systems Inc.) is used in detecting targets, identifying the endmembers, preliminary analysis as (i) Minimum Noise Fraction (MNF) transformation - to determine the inherent dimensionality of image data and, (ii) Pixel Purity Index (PPI) spatial reduction by finding the most spectrally pure or extreme pixels (correspond to mixing endmembers) in the image data, and (iii) n-Dimensional Visualization - interactive identification of endmembers by locating and clustering the purest pixels in the scatter plot which

conveys the shape of the spectrum for a single pixel.

2.2 Sub-pixel Mapping

The study approach is based on the phenomenon of spatial dependence, also commonly referred to as spatial correlation or autocorrelation amongst the various vegetation classes and more subjective to the phumdis class. The key problem of sub-pixel mapping is in determining the most likely locations of the fractions of each wetland cover class within the pixel. It is assumed that the wetland cover is spatially dependent both within and between pixels in the image. Such an assumption is realistic on condition that the intrinsic scale of spatial variation in each wetland-vegetation cover class is the same as or greater than the scale of sampling imposed by the image pixels (Atkinson, 1997).

If formulated as case of assignment problem, suppose the linear pixel unmixing module yields fraction images for NLC land cover classes and the coarse resolution pixels is to be divided into NP sub-pixels. The numbers of sub-pixels that have to be assigned to land cover class i is NPLCi and has been derived from the fraction images. A measure for spatial dependency PLCij has been calculated for land cover class i and each sub-pixel j. Each sub-pixel has to be assigned a value 1 or 0 for each land cover class, 1 indicating an assignment to the particular land cover class. Thus in the present study, j is assigned the value 1 for the phumdis class and 0 for the non-phumdis class. The problem of assigning vegetation classes to the sub-pixels while maximizing the spatial dependency is solved using the equality constraints. To construct the mathematical model choice variables, xij are defined so that

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, 1 for sub-pixel j assigned to wetland vegetation class i, 0 otherwise. Then the mathematical model assignment can be expressed as,

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In this study, the in-built ENVI program is adopted for assigning selected endmembers to ordered spatial locations and the sub-pixel based mapping besides SAM used are Mixture-Tuned Matched Filtering (MTMF) and Linear Spectral Unmixing. 2.3 Data Used In the present study, multi-temporal satellite images of the IRS (Indian Remote Sensing Satellite) 1C /1D with LISS-III multi-spectral sensor providing a spatial resolution of 23.5m, are used. Images of 1998, 2002 and 2006 (all dated in the cloud-free winter months Jan-Feb) are subset using a common AOI. In addition, to improve the adequacy in defining the wetland boundary for the AOI, IRS-PAN image of 5.8 m spatial resolution is used. For validation of the use of the sub-pixel mapping methods viz., MTMF and Linear Spectral Unmixing along with SAM, as to validate the mapped or classified pixel locations with special reference to the phumdis, IKONOS Geo 1m PAN image of 2003 is also used. Prior analysis and processing, the images are re-projected to the same co-ordinate system as: Projection Geographic (lat\lon), Spheroid Everest, Datum Everest. These images were considered completely free of missing values as cloud, noise, etc. Ground truth and

ancillary data as Loktak vegetation cover maps were also used to aid in preliminary identification of the various vegetation species encompassing Loktak pat. 2.4 Spectral Processing and Analysis Spectral processing in ENVI leverages the unique spectrally over-determined nature of the imagery and provides several methods for mapping the spatial location of each of the selected endmember spectra. Collection of endmembers is tried for three aspects focused on the phumdis vegetation class: (i) image based endmember selection, (ii) measured endmember spectra and (iii) user-defined (modeled) endmembers. Pre-unmixing allows for identification of purer endmembers than the initial ones. This process can be repeated until a certain pixel purity index (PPI) or purity-criteria is reached. Upto 8000 iterations are adopted to obtain respective PPI criteria. In this study, the PPI was performed using the 4 MNF bands. The PPI was run on the MNF transformed data (spectral reduction by using two cascaded Principal Component transformations) to aid in deriving endmembers from the image. Then the n-Dimensional visualizer was loaded with the top scoring pixels from the PPI result. The inherent dimensionality of the data (determined by the eigenvalues and MNF images) is adjusted to a spatial coherence by using all of the MNF bands that have any reasonable image quality of eigenvalues well above unity. The spectral data can be seen in many dimensions from many angles from which interactive selection of end-members is done. A SAM Maximum Angle Threshold of 0.10 degree is kept for all the analysis, as smaller angles represent closer matches to the reference spectrum.

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RESULTS AND DISCUSSIONS

The image based approach of selecting endmembers shows scattered Loktak wetland portions with phumdis abundance whereas none or less abundance of this endmember can be found in the same portions in the measured or user-defined endmember selection approaches. The spotted (dot-like) features, at a more or less recurrent spatial density against a contrasting background (water spectra), represents well-mapped phumdis. The image-derived endmember spectra show highest abundances of the floating phumids class for all three mapping methods, although the abundance values differ. This is shown by the MTMF scatter plots (fig.3), where the MF scores range between (i) -0.71 to 0.94 for 1998, (ii) -0.36 to 0.40 for 2003, and (iii) -0.44 to 1.08 for 2006. The temporal and spatial variation for the same sensor image dataset is used to arrive at the dynamic change in Loktak wetland cover, and more specifically to the phumdis class distribu-tion. Results of the PPI pixel scores (conducted on a 10,000 pixel cluster set) of 31.15%, 40.33% and 25.41% respectively for the temporal periods indicate that only few pure pixels relate to the phumdis class and the degree of mixed pixels needs interactive process for the target endmember selection. For each prominent Loktak wetland vegetation class, the fraction of each indicator group estimated from the sub-pixel classification is compared with that calculated from the vegetation map (ancillary data), so the training data is excluded from the analysis. Visually, the spatial distribution of the phumdis indicator groups corresponded well with what would be expected from the mapped information (figs. 4 to 6). The athaphums (cultivated phumdis) indicator group occurs most frequently in the areas mapped as the high phumdis content classes with a scattering

throughout classes. There are few pixels classified as non-phumdis class for the land-locked or island areas of Loktak wetland.

Fig.3. MTMF scatter plots for the respective temporal images (a) 1998, (b) 2003 and (c) 2006 The MTMF mapped results (fig.4) present phumdis highlighting well-defined vegetation changes for the temporal distribution used in the study. Although some incorrectly mapped pixels beyond the matched filter score is likely to occur wherever composite phumdis and non-phumdis background distribution is encountered, a noise-limited spread and low infeasibility values is obtained. The spectrally linear unmixed results show that proportions of different wetland vegetation

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were approximated from each image pixel and algorithm generated images where pixel values represented the proportions of phumdis class (fig.5). The SAM classified results present a distinct representation of the phumdis which are well distributed spatially. In some portions, phumdis prevailing in a cluster set in Loktak wetland show a cloud or noise-like feature. Bright image pixels represent small values of spectral angle and dark pixels represent wide

spectral angles. Small spectral angles mean that the reference spectrum describes the study area well. The more there are bright pixels in the spectral angle images the better choice that reference spectrum is for describing the test area. Thus, a sharp phumdi feature mapped result is mostly observed in the lake central zone (fig. 7A) where each phumdi is founded on clear water backdrop.

SCALE

Fig. 4. MTMF mapped Loktak wetland cover (a) 1998, (b) 2003 and (c) 2006

Fig.5. Linear Spectral Unmix mapped Loktak wetland cover (a) 1998, (b) 2003 and (c) 2006

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Fig.6. RMSE images of sub-pixel spectral unmixing (Loktak wetland) (a) 1998, (b) 2003 and (c) 2006

Fig.7A. Spectral Angle Mapped images of Loktak wetland cover (a) 1998, (b) 2003 and (c) 2006

Fig.7B. SAM mapped (composite image) of Loktak wetland highlighting phumdi cover

It is observed that pixels as pure as the measured or user-defined endmembers are hardly found when there is a limitation in the no of bands in the hyperspectral image data (4 band IRS-1C and 1D). Therefore, the average abundance value for each of the three temporal data is low. It remains difficult to define a representative end-member spectrum. Defining endmember spectra on the basis of availability of a range of parameters describing the growth-decay status of the phumdis and the structure of its canopy, needs to be incorporated for better results. Applications in wetland vegetation studies have to take into account these effects, since imaging sensors with large

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swath angles produce data under differing viewing conditions. Note that the actual spectral angle calculation is based on all of the bands in the image. Further the SAM classified results (fig.7A) indicate a mixed or undefined feature for phumdis prevailing in a mixed vegetation space; this shows that we need more than the direction of vector in order to separate the phumdis-contributing vegetation species from the non-phumdis species, which are spectrally similar in nature. Furthermore, the complexity of SAM requiring many parameters to be defined could still be sub-optimal for the high biodiversity conditions ascribing Loktak wetland. The RMSE results (fig.6) of the spectral unmixing in the sub-pixel mapping process show regular dots on most fractions of the image showing spatially incoherent features in the error image that less correspond to real features on the ground. The approach taken here of validating the classification on a percent cover basis, is done by using higher resolution ground truth data over a small area to test the accuracy of the derived component fractions is achieved by calculating the sample correlation coefficient R, which is the estimator of the correlation coefficient r, between the known wetland cover proportions from the validation IKONOS image and the sub-pixel mapped output. An overall accuracy of 52.22 % as based on the sample R is computed from five different fractions (zones) of Loktak wetland. In a random choice of 60 set of phumdis features, the scatter of SAM mapped values with the high resolution IKONOS image are found to lie within the 20 % deviation line from the perfect agreement line (R2 = 1), as shown in fig.8. This is within acceptable limits as SAM classifica-tion assumes reflectance data. Further studies to improve the use of the mixed and sub-pixel classifiers will be conducted to enhance the applicability of such methods.

Fig.8. Validation assessment of SAM results - Loktak wetland phumdis

CONCLUSIONS The study emphasizes that use of phumdi cover statistics derived from the multiple-date data from which it was possible to develop successional phumdi-distribution maps for Loktak pat entity. Some of the conclusions that can be derived from the study:

Differences of abundances of the phumdis endmember in the same lake zone arises due to the different growth-decay cycle of phumdis, as also interpreted from the respective three endmember selection approaches.

The sub-pixel mapping approach adopted can be enhanced by using in-situ reference measurements of endmember spectra which may offer the possibility to retrieve biochemical and biophysical parameters (with special reference to the phumdis vegetation class) from imaging data. Respective reflectance (spectro-meter) ground data for the prominent Loktak wetland plant species need to be acquired for more efficient studies.

The impact of shadowing (especially in the eastern boundary and KLNP where mixed-swamp vegetation occur) implies that where the sub-pixel classification overestimates the occurrence of phumdis

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class vegetation species, it will tend to underestimate the occurrence of the other non-phumdis and invasive class species.

The mapped results show quite an extent of variation in the pixel values of the same phumdis and non-phumdis class. Phumdis features are best represented in canopy-based mapping - SAM.

The validation of the SAM mapped result with the higher resolution IKONOS image show an acceptable 20% distribution from the perfect agreement line.

The dynamic nature changes of phumdis in Loktak wetland would necessitate the consistent use of satellite-based remote sensors and low-cost affordable geospatial techniques for effective wetland conservation in this remote terrain of the country. This study shows clearly that it is worthwhile to pay attention to different methods when the reference spectra in pixel components are calculated. As the lake resources is under severe stress from prolific growth of phumdis, classifying and mapping Loktak wetland based on spectral response of pure pixel components for a class, should be rationale to describe the ecological standpoint in inventory and management of the Loktak wetland system.

REFERENCES

Atkinson P.M., 1997. Mapping Sub-pixel boundaries from Remotely Sensed images, in: Z. Kemp (Ed.), Innovations in GIS 4, pp. 166-180. Chang Chein-I, 2006. Recent Advances in Hyperspectral Signal and Image Processing, Transworld Research Network, USA. Harvey K.R. and Hill G.J.E., 2001. Vegetation Mapping of a Tropical Freshwater Swamp in the Northern

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Kneubuehler M., Schaepman M.E. and Kellenberger T.W.,1998. Comparison of Different Approaches of Selecting Endmembers to Classify Agricultural Land by means of Hyperspectral Data, IEEE Transactions on Geosciences and Remote Sensing, pp. 888-890.

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Shafri H.Z.M., Suhaili A. and Mansor S., 2007. The Performance of Maximum Likelihood, Spectral Angle Mapper, Neural Network and Decision Tree Classifiers in Hyperspectral Image Analysis, Journal of Computer Science, Vol. 3,No.6, pp.419-423. Singh, Ng. R., 2006. Fluvial Regime of the Manipur River Basin and Loktak lake with study of Backflow, M.Tech Thesis, WRDM, Indian Institute of Technology Roorkee, Roorkee, India.

Singh, Ng. R., Singh S.K. and Sharma N., 2007. Fluvial Patterns in the Loktak Lake Sub-basin through Two Interlinking Channels, Proceedings, TAAL 2007 - XII World Lakes Conference , 28th Oct -2nd Nov 2007, Jaipur, India.

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Laboratory of Forest Management and Spatial Information Techniques, University of Gent, Belgium.

WAPCOS, 1993. Detailed Project Report for development of Loktak-Lake Sub Basin, Manipur, Water and Power Consultancy Services Pvt. Ltd. Case Study Report, New Delhi, India.

WISA and LDA, 2004. Atlas of Loktak, citation by Trisal C.L. and Manihar Th., published jointly by Wetlands International - South Asia Programme (WISA) and Loktak Development Authority (LDA) under the SDWRML project, aide of Indo-Canadian Environment Facility (ICEF).

ACKNOWLEDGEMENTS

The data used in the present study were provided by the Loktak Development Authority (LDA). Some data were provided by the Manipur Wetlands Society (MAWETS) and the Earth Sciences Department, Manipur University. The writer wishes to thank the organizations and the staff of the LDA, in particular Sanajaoba Ng. and Bidhan Ch., who were associated with data observation, processing, and management of database. The writer also acknowledges the support of his friends in ground record assessments.