Academic journal article Electronic Green Journal

Wetlands of Greater Bangalore, India: Automatic Delineation through Pattern Classifiers

Academic journal article Electronic Green Journal

Wetlands of Greater Bangalore, India: Automatic Delineation through Pattern Classifiers

Article excerpt

Wetlands are the most productive and biologically diverse but very fragile ecosystems. They are vulnerable to even small changes in their biotic and abiotic factors. In recent years, there has been concern over the continuous degradation of wetlands due to unplanned developmental activities. This necessitates inventorying, mapping, and monitoring of wetlands to implement sustainable management approaches. The principal objective of this work is to evolve a strategy to identify and monitor wetlands using temporal remote sensing (RS) data. Pattern classifiers were used to extract wetlands automatically from NIR bands of MODIS, Landsat MSS and Landsat TM remote sensing data. MODIS provided data for 2002 to 2007, while for 1973 and 1992 IR Bands of Landsat MSS and TM (79m and 30m spatial resolution) data were used. Principal components of IR bands of MODIS (250 m) were fused with IRS LISS-3 NIR (23.5 m). To extract wetlands, statistical unsupervised learning of IR bands for the respective temporal data was performed using Bayesian approach based on prior probability, mean and covariance. Temporal analysis of wetlands indicates a sharp decline of 58% in Greater Bangalore attributing to intense urbanization processes, evident from a 466% increase in built-up area from 1973 to 2007.

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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 populations, 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. The spectral signature associated in each pixel of the remotely sensed data is used to perform the classification and, indeed, is used as the numerical basis for categorization of various spatial features (Lillesand & Kiefer, 2002). Most of these classifications are based on certain pattern recognition techniques. Among the various frameworks in which pattern recognition has been traditionally formulated, the statistical approach has been most intensively studied and used in practice. The design of a recognition system also involves the following issues: definition of pattern classes, sensing environment, pattern representation, feature extraction and selection, cluster analysis, classifier design and learning, selection of training and test samples, and performance evaluation. Pattern recognition techniques such as neural network, decision tree, fuzzy theory, etc. have been widely used with RS data to identify the patterns in land use classes like urban, agriculture land, etc (Kwan & Cai, 1994), (Fukushima, 1998), (Gori & Scarselli, 1998) and (Lee, Liu, & Chen, 2006). One of the primary applications of pattern classification is feature extraction. Extraction of land cover features of interest from remotely sensed data leads to a number of applications for decision makers to management planners. Given an image, the classifiers can be used to categorize the image into user defined types or to identify features based on their inherent patterns. …

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