Performance evaluation of vegetation indices using remotely sensed data

Vegetation is one of the most important components of the ecosystems. Knowledge about variations in vegetation species and community distribution patterns, alternations in vegetation phonological (growth) cycles, and modifications in plant physiology and morphology provide a valuable insight in to the climate, geologic and physiographic, characteristics of an area, so mapping vegetation cover is important to understand various ecological processes occurring in nature, their relationship and the various geomorphological changes occurring over the period of time. Remote sensing is the advanced tool for surveying. It provides the synoptic view of the area. Remote sensing satellites provide data with different spatial, spectral, radiometric and temporal resolutions. Based on requirement and level of study the different data products can be used for mapping. Mapping vegetation through remotely sensed images involves various considerations, processes and techniques. Increasing availability of remotely sensed images due to the rapid advancement of remote sensing technology expands the horizon of our choices for imagery sources. Various sources of imagery are known for their differences in spectral, spatial, radiometric and temporal characteristics and thus are suitable for different purposes of vegetation mapping. Various techniques have been developed to map the vegetation with varying accuracies and cost. The simplest one is to use vegetation indices, they are easy to understand and calculate. Various vegetation indices have been designed and have their specific utility for vegetation mapping. Some vegetation indices not only help to identify vegetation but also help to identify and map other land cover classes. The present paper will throw light on various vegetation indices developed, their applications and provide a detailed comparison study for their mapping efficiency based on spatial resolution of data used. This study reveals that NDVI gives the better result in terms of overall accuracy to assess the vegetation.