ASSESSING VEGETATION PATTERN USING MODERATE RESOLUTION IMAGING SPECTRORADIOMETER (MODIS) IMAGES ALONG THE WESTERN COASTAL AREA OF BANGLADESH

The coast of Bangladesh is known as a zone of vulnerability. It is prone to natural disasters like cyclones, storm surges, and floods. Moderate Resolution Imaging Spectroradiometer (MODIS) images with spatial resolutions ranging from 250 to 1 km are primarily used to assess vegetation dynamics and processes at a large scale. Using pixel-based maximum likelihood classification (MLC) on these data can produce products with an accuracy ranging from 63% to 82%. The extraction of vegetation information from satellite images is based on interpretation factors such as color, texture, tone, pattern, and association. Many sensors provide imagery for producing VI (e.g., Normalized Difference Vegetation Index or NDVI) calculated from the bands in the visible and near-infrared regions. A good technique that has the potential to improve vegetation classification is the fusion of remotely sensed data with multiple spatial resolutions. The efficient integration of remote sensing information with varying temporal, spectral, and spatial solutions is necessary for accurate vegetation mapping. NDVI values range from +1.0 to -1.0. Areas of barren rock, sand, or snow usually show shallow NDVI values (for example, 0.1 or less). Sparse vegetation such as shrubs, grasslands, or senescing crops may result in moderate NDVI values (approximately 0.2 to 0.5). High NDVI values (about 0.6 to 0.9) correspond to dense vegetation such as that found in temperate and tropical forests or crops at their peak growth stage. The classification of Land use and Land cover of the Western coastal area of Bangladesh shows that there are eight different sectors: water, dense vegetation, grassland, flooded vegetation, agricultural land, shrub land, built-up area, and bare land. Using MODIS, greater than 6000 and 5374 pixels represent dense and sparse vegetation areas respectively. The NDVI found that the sparse vegetation of the western coastal area is increasing, but the site's dense vegetation is decreasing from 2003 to 2022. This study validates the importance of a thorough understanding of the related concepts and careful design of the technical procedures, which can be utilized to study vegetation cover using remote sensing images.

Keywords: vegetation mapping, remote sensing sensors, image processing, image classification, and coastal area