Abstract
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 data. Pattern classifiers were used to extract wetlands automatically from NIR bands of MODIS and Landsat remote sensing data. MODIS provided data of 2002 to 2007, while for 1973 and 1992, IR Bands of Landsat (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 indicate sharp decline of 58% in Greater Bangalore attributing to intense urbanisation process, evident from 466% increase in builtup area from 1973 to 2007.
Highlights
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
In unsupervised learning, where no previous estimation parameters were available, quite motivating and realistic results were obtained in extracting water bodies and their extent using IR bands of remote sensing (RS) data
There were 159 water bodies spread in an area of 2003 ha in 1973, that number declined to 147 (1582 ha) in 1992, which further declined to 107 (1083 ha) in 2002, and there are only 93 water bodies with an area of 918 ha in the Greater Bangalore region in 2007
Summary
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. The spectral signature associated in each pixel of the remotely sensed data is used to perform the classification and, 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. The design of a recognition system involves the following issues: definition of pattern classes, sensing environment, pattern representation, feature extraction and selection, cluster analysis, classifier design and learning, Electronic Green Journal, Issue 26, Spring 2008 ISSN: 1076-7975 selection of training and test samples, and performance evaluation. Pattern recognition techniques such as neural network, decision tree, fuzzy theory, etc. This paper focuses on identification of wetlands using unsupervised pattern classifiers
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