INTRODUCTION

Wetlands are an essential part of human civilization, meeting many crucial needs for life on earth such as drinking water, protein production, energy, fodder, biodiversity, flood storage, transport, recreation, and climate stabilizers. They also aid in improving water quality by filtering sediments and nutrients from surface water. Wetlands play a major role in removing dissolved nutrients such as nitrogen and to some extent heavy metals (Ramachandra, 2002). They are becoming extinct due to manifold reasons, including anthropogenic and natural processes. Burgeoning population, intensified human activity, unplanned development, absence of management structures, lack of proper legislation, and lack of awareness about the vital role played by these ecosystems are the important causes that have contributed to their decline and loss. Identifying, delineating, and mapping of wetlands on a temporal scale provide an opportunity to monitor the changes, which is important for natural resource management and planning activities (Ramachandra, Kiran, & Ahalya, 2002). Temporal RS data coupled with spatial analysis helps in monitoring the status and extent of spatial features.

Extracting spatial features such as wetlands (which include lakes, ponds, tanks, marshy areas, etc.) from temporal RS data helps in monitoring their status including spatial extent, physical, chemical, and ecological aspects. Traditional approaches are digitizing through visual investigation, applying a density slicing method or an edge detection method to a single band, and classification using multiple bands (Kevin & El Asmar, 1999). However, extraction of features in now possible with multi-sensor multi-resolution images acquired from the earth observation satellites.

Earth observation satellites provide data covering different portions of the electromagnetic spectrum at different spatial, spectral, temporal and radiometric resolutions giving a more complete view of the observed objects. However, two major factors limit sensor’s ability to collect high spatial resolution (HSR), Multispectral (MS) data. First, the incoming radiation energy to sensor is limited by optics size. Second, the data volume to be collected and stored by the sensor increases exponentially with HSR. With physical and technological constraints, some satellite sensors supply the spectral bands needed to distinguish features spectrally but not spatially, while other satellite sensors supply the spatial resolution for distinguishing features spatially but not spectrally. For many applications, combination of data from multiple sensors provides more comprehensive information. Thus, satellites such as QuickBird, IKONOS, IRS bundle a 1:4 ratio while Landsat and SPOT bundle a 1:2 ratio of a HSR Panchromatic (PAN) band and low spatial resolution (LSR) MS bands in order to support both colour and best spatial resolution while minimising on-board data handling needs. A critical consideration is how to integrate spatial information present in the PAN image but missing from the LSR MS data. Therefore, for full exploitation of increasingly sophisticated multi-source data, advanced analytical or numerical image fusion techniques are required.

Image fusion refers to combining the geometric detail of a HSR PAN image and the spectral information of a LSR MS image to produce a final image with the highest possible spatial information content while preserving good spectral information quality. It describes a group of methods and approaches using multi-source data of different nature to increase quality of information contained in the data. Fused images provide increased interpretation capabilities, more reliable results as data with different characteristics are combined, reduces ambiguity, improves reliability, improves classification, substitutes missing information and are also used for feature extraction, flood monitoring, ice/snow monitoring, geological applications, etc.

However, for a particular application, it is necessary to have apt spectral and spatial resolution, which is a constrain by availability. Availability depends on the satellite coverage, operational aspects, atmospheric constraints such as cloud cover, economic issues, suitable fusion level, geometric model, ground control points, re-sampling method etc. Considering all these aspects, an attempt has been made to evaluate the performance of five image fusion techniques such as SFIM (Smoothing Filter), COS (Component Substitution), High Pass (HP) Fusion, High Pass (HP) Filter and High Pass Modulation (HPM) when applied on different resolution ratios (PAN and MS obtained from different sensors), such as (i) Fusion of 1:4 resolution ratio (IRS PAN 5.8 m + LISS-III MS 23.5 m), (ii) Fusion of 1:2 resolution ratio (Landsat ETM + PAN 15 m + MS 30 m), (iii) Fusion of 1:50 resolution ratio (IRS PAN 5 m + MODIS 250 m), (iv) Fusion of 1:100 resolution ratio (IRS PAN 5 m + MODIS 500 m), (v) Fusion of 1:250 resolution ratio (IKONOS PAN 1 m + MODIS 250 m), and (vi) Fusion of 1:500 resolution ratio (IKONOS PAN 1 m + MODIS 500 m).

The main objectives of this study are

1. to find out the best technique for fusing images of different resolution ratios in order to achieve both high spatial and high spectral resolutions.
2. to carry out spatial and temporal analysis of wetlands (change during the period 1973-2007) in Greater Bangalore through pattern classifies to understand responsible causal factors and the likely implication of these dynamics.

The paper is organised as follows. Section 2 discusses data followed by methods in section 3. Results and discussion is presented in section 4 followed by concluding remarks in section 5.