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Mapping and Analyzing Vegetation Types of North Andaman Islands, India
P.Rama Chandra Prasad
National Collateral Management Services Limited,
Hyderabad, India.
Ch. Sudhakar Reddy, G.Rajasekhar
Forestry and Ecology Division,
National Remote Sensing Agency, Dept of Space,
Balanagar, Hyderabad, India – 500037.
C.B.S.Dutt
Indian Space Research Organization,
Dept of space, Anthariksh Bhavan,
Bangalore, India.
ABSTRACT
A detailed vegetation map is required for the effective management of natural resources. An attempt was made for the first time to prepare vegetation type of north Andaman Islands using the high resolution LISS III satellite data. In the present study visual interpretation along with digital supervised classification aided in preparation of more accurate and precise vegetation type map of north Andaman Islands. In digital classification the accuracy of map was found to be low due to limited spectral bandwidth available with IRS 1C/1D satellite data, which is about 80 – 100 nano meters. Patch characterization studies showed presence of large patches in semi evergreen forest which forms dominant as well as diversified community of the study area.
Abbreviations
IRS – Indian Remote Sensing, DEM- Digital Elevation Model, LISS – Linear Imaging Self Scanner, IGBP – International Geosphere Biosphere Programme, GPS – Global Positioning System.
Introduction
Vegetation acts as an integrator of many of the physical and biological attributes of an area, (Specht 1975, Austin 1991) and a vegetation map can be used as an approach for vegetation evaluation. International Research Programes like IGBP, mainly aims at the global scale vegetation mapping (Mayaux et al., 2002) and one of the important objectives of vegetation mapping is to define, distinguish vegetation units (Nilsen and Elvebakk, 1999) for the effective management of natural resources (Trisurat et al., 2000). The advent of remote sensing and GIS techniques facilitated the researchers to carry out detail studies pertaining to vegetation type mapping, their distribution, modeling, and endemic species habitat zonation etc.
Many studies revealed the process of mapping using imagery from different dates, resolutions, sensors and classifiers. Porwal and Pant (1989) used visual interpretation technique for the forest cover type mapping by combining 2, 3, 4 bands of Landsat TM data for Charkrata in western Himalayan region, India. Roy et al (1991) adopted both visual and digital technique for mapping forest cover in parts of Assam region using Landsat MSS data. Initially visual interpretation was adopted to stratify the forest types than supervised classification using maximum likelihood technique for further detailed mapping. Visual interpretation of Landsat TM for characterizing ecological parameters in tropical forest of Bakultala range, middle Andaman was done by Roy et al., (1993). Kimes et al (1999) used SPOT HRV images for mapping secondary tropical forest using unsupervised classification in conjunction with neural network with an overall average accuracy of 95.2%. Neig et al (1999) visually interpreted IRS-1B LISS II supplementing with Landsat TM and SPOT data to increase the accuracy of the map. Gao (1999) carried out an interesting study by comparing the spatial and spectral resolutions of satellite data for mapping mangrove forests in western waitemata harbour, New Zealand. Mapping was done from SPOT HRV and Landsat TM at 10, 20 and 30 m resolution by maximum likelihood method and found that accuracy of mapping is more for Landsat TM. Sudhakar et al (1999) mapped forest types of Jaldapara wildlife sanctuary using IRS-1B LISS II by three classifiers viz. maximum likelihood, contextual and neural network. They proposed the neural network classifier to be the best in assessing high accuracy followed by maximum likelihood. Jaykumar et al (2000) showed that visual interpretation of Landsat TM for delineating forest types was most effective classification. Khoruk et al (2003) adopted a technique of classifying AVHRR images using IDRSI software, where data was subjected to segmentation process followed by supervised classification.
The present study mainly focuses on preparation of detailed vegetation type map and analyzes the forest patch characters and their distribution with in North Andaman Islands. Assessment of forest patch characters with reference to size and numbers provides information on the species richness, composition, abundance and diversity pattern with in particular forest type.
Study area
North Andaman, the present study site, is one of the important district of Andaman & Nicobar archipelago, a group of green islands, found floating in deep blue Indian Ocean.. They are located between 12°.95’ N and 92°.86’ E, constituting about 70 large and small islands. The terrain is rough with hills enclosing narrow longitudinal valleys formed of territory sand stone, lime stone and shale. Soils are derived from sandstones, serpentines, conglomerates and are acidic non calcareous with low organic matter and high nitrogen content. Lush forest vegetation is found in these islands due to continuous rainfall brought by monsoons with a short dry period. As per the champion & Seth (1968), the study area has been classified as Andaman evergreen (1A/C2), Andaman semi evergreen (2A/C1), Andaman moist deciduous (3A/C1), Littoral (4A/L1) and Mangrove forest (4B/TS2).
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