Land cover mapping relates to identifying the types of features present on the surface of the earth. It deals with discerning the extent of land cover features namely vegetation, geologic, urban infrastructure, water, bare soil or others. Variations in land cover and associated physical characteristics do influence weather and climate of our earth and hence, it is considered an essential element for modelling and understanding the earth as a system for many planning and management activities. Thus, understanding of land cover dynamics plays an important role at the local/regional as well as global level. Identifying, delineating and mapping land cover on temporal scale provides an opportunity to monitor the changes, which is important for planning activities and sustainable management of the natural resources.

Land cover mapping can be done most effectively through space borne remote sensors of various spatial, spectral and temporal resolutions. Due to the spectral resolution limitations of conventional multispectral imageries, hyperspectral sensors, which collect numerous bands in precisely defined spectral regions were developed. Hyperspectral images have ample spectral information to identify and distinguish spectrally unique materials that allow more accurate and detailed information extraction. These imageries are classified into different land cover categories using various algorithms. The genesis and the underlying principle behind each of these algorithms are different and essentially produce different output maps. This paper discusses the various efforts made for land cover and land use mapping with an emphasis on the hard classification algorithms for hyperspectral image processing at a regional scale. Neural network algorithm for classifying MODIS data has been implemented for Kolar district, Karnataka. The accuracy assessment is done using ground truth data and classified multispectral map on a pixel to pixel analysis .

Keywords: Land cover, hyperspectral, MODIS, algorithms