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Mapping and Analyzing Vegetation Types of North Andaman Islands, India
Materials and Methods IRS 1C/1D LISS III data of 1st March 1999, 115 / 064 path and row with resolution of 23.5 m and 4 (2,3,4,5) bands viz, Blue (0.45– 0.52), Green (0.52-0.59), Red (0.62-0.68) and Infra Red (0.77-0.86), was used for vegetation mapping. (Fig: 1).The geometrically rectified image was subjected to the process of classification using sequential steps by unsupervised, supervised and visual interpretation techniques for the generation of final vegetation map of the study area (Fig .1). Initially image was classified by unsupervised isoclustering method to separate the pixels of the image into different spectral clusters representing various land use / land cover types. Hard copy of the spectral cluster map was generated for reconnaissance field survey to get acquainted with the general pattern of vegetation of the area and to identify the spectral clusters representing different features on ground. Traverses, along all roads and major drainage, hill tops, creeks and sandy beeches were made for collecting ground truth. The existing literature survey and interaction with forest department and local institutions was also made for collecting knowledge base. During the field survey the geographical coordinates of the predominant vegetation types and other land cover classes were marked on the cluster map using GPS instrument.  Fig: 1 False Colour Composite and Vegetation type map of North Andaman Later image was classified digitally by the technique of supervised classification using maximum likelihood classifier, with appropriate signatures/training sets generated from half of the ground control points collected during field inventory, for corresponding land cover and vegetation classes. A thematic map representing various land cover and vegetation type classes was prepared and random samples plots were generated in each vegetation type for field sampling. The detailed field inventory for phytosociological data collection include laying quadrant of 0.1 ha size in selected location and gather information about trees, climbers, shrubs, herbs, saplings and seedlings encountered within the quadrant. The collected field data was further used for updating map with the minor variations that are observed during the field survey, finally generating vegetation type map of the study area. Accuracy assessment of the prepared map was performed by overlaying the field sample points corresponding to vegetation types as well as remaining ground control points collected during pre classified field inventory over the classified map. The accuracy of the map was found to be 71 % due to spectral mixing and overlapping between vegetation classes (Table-.2;).Hence to achieve for better performance, image was further reclassified using visual interpretation technique. A sequential interpretation method was adopted for separating different classes step by step using field knowledge and digital classified map.  Fig: 2. Visual vs. Digital method comparative forest area statistics In the first step all the non-forest classes viz. agriculture / settlement, barren, fallow, mudflats, water bodies were masked out. Next in the second step, coastal vegetation including mangroves and littoral forest were separated based on their location and typical characteristic spectral tone. Finally in third step interior forests viz. evergreen, semi-evergreen and moist deciduous types were delineated. Based on the available ancillary data and prior ground knowledge, image classification was done using on screen visual interpretation technique by digitizing and labeling polygons for various classes using Arc View 3.2.1. The multi spectral characteristics of the different forest types i.e. variation in tone, colour, texture, shade, site, of various objects with in the satellite data formed the basis for classification. The altitude as well as aspect maps produced from DEM were also used as supporting data to locate the altitudinal vegetation types and to identify the hill shade regions while interpreting. The interpretation key used for identifying various vegetation classes on image is depicted in Table-1.  Fig: 3. Buffer Zones and Forest Patches The final vegetation vector layer generated from hybrid approach was used for patch characterization. To identify the spatial distribution of various forest patches through out the north Andaman, five buffer zones were created starting from coast line with an interval of 1500m, proceeding towards interior forest (> 6000 m from coast). Later with in each forest type patches were categorized into six classes with an interval of 50 ha. This kind of analysis gives the scenario of forest spread at zero meters altitude of coast to 732 m high altitude of interior forest as well as location of large size patches that harbor high species richness. Results and Discussion The ultimate result of the classification is to distinguish the area into various forest and non-forest categories. Important vegetation types of the study area include evergreen, semi evergreen, moist deciduous, littoral, dense, degraded and open mangrove (Fig: 1). Water class was excluded from the total area statistics. Semi evergreen forest observed as dominant vegetation type of the north Andaman by both the interpretation methods. Visual technique helped in the delineation of additional stunted evergreen / southern hill top evergreen forest class (later merged with evergreen), and various sub classes within mangrove forest based on their species composition as Rhizophora, Brugeria community etc., due to the variation in spectral values and prior knowledge of the area which could not be achieved by digital method. The accuracy as well as delineation of various classes in visually interpreted map was found to be high (85%) and this was achieved mainly by the supportive information obtained from the digital technique. The hybrid classification approach using both digital and visual methods along with the ground phytosociological data aided in producing better vegetation map of the study area. Accuracy assessment was performed only for the predominant vegetation types, since coastal vegetation (Mangroves and littoral) are easily separable (Table- 2). Observations
  The low mapping accuracy in case of digital classification approach compared to visual was due to the mixed pixel problem. In certain areas on the image there was spectral overlap between vegetation classes, which could not demarcate digitally the predominant evergreen class from semi evergreen, and semi evergreen from southern hill top evergreen (high altitude stunted evergreen forest). The reason for increase in moist deciduous forest area in digital classification is overlapping of semi evergreen, littoral and water pixels in few areas, while in visual, experience and knowledge regarding the distribution of the type made it possible to delineate the classes distinctly from each other. The structure of forest mainly depends on the physiognomy (height) and species composition of forest type. The digital classification technique is primarily based on the spectral reflectance emitted by various representative species groups of forest on ground. In general the semi evergreen forest shows species composition of both evergreen and semi evergreen species. Depending on the topography, rainfall and soil types, the predominant top canopy species varies and either of the species may emerge as top canopy species group. The observed spectral overlap between evergreen and semi evergreen indicates that in those areas of semi evergreen forest the top canopy is formed by evergreen species, which could not be spectrally separable by digital method and similar is case with the moist deciduous forest. The main reason that could be attributed for the increase in the area in visual interpretation when compared to digital method is a more precise delineation of the classes like Littoral, mud flats, plantations, mangroves and sand at the edges, along the coast and in between the islands. Visual interpretation was carried out using the knowledge of training sets provided by digital technique, field inventory, topographical features, location of forest types, drainage pattern which helped in producing a better vegetation type map of the study area. Both the methods proved to be better in delineating a homogenous core area patches and digital process failed in drawing clear patch boundaries at edges. The visual interpretation technique is advantageous in detecting spatial patterns and in drafting precise boundaries around relatively homogenous area while the digital methods typically operates one pixel at a time (Estes et al., 1983). Although the training areas were refined in digital supervised classification, there was no change in the seperability even with increasing or changing the number of pixels in the training areas resulting in the misclassification of pixels. Perhaps digital classification algorithm exclusively depends on the spectral reflectance of the ground features without considering topographic factors. |