Effects of topographic corrections on MODIS sensor satellite imagery of mountainous region

Geographic Information System (GIS) plays a vital role in collecting or analyzing land-use/cover change information for environmental impacts and infrastructure management. One of the major problem associated with high spatial resolution (56 square meter/pixel) sensors such as AWiFS that is characterized by low temporal resolution (5 days/cycle), limits the application of remote sensing. The medium spatial resolution (250 square meter/pixel) sensors such as MODIS (1-2 days/cycle), have resolved the problem of low temporal resolution up to a great extent by compromising with spatial resolution. It enhanced the capability of remote sensing to deliver data of larger areas on daily basis. To enhance raw satellite imagery, the preprocessing corrections are crucial to make it appropriate for a specific application such as change detection, resource monitoring or classification etc. It comprises geometric corrections, radiometric correction or atmospheric correction for flat surface terrain. But for mountainous region, topographic corrections are also necessary to be performed on satellite imagery before further consideration. The prominence of topographic corrections are very rarely investigated on land cover mountainous region using MODIS imagery. In present paper, necessary preprocessing corrections along with topographic corrections implemented on MODIS sensor satellite imagery of mountainous region. The consequences are validated by realizing preprocessed topographically corrected and without topographically corrected MODIS sensor satellite imageries on supervised Maximum Likelihood Classifier (MLC). Experiments outcomes confirms that topographically corrected MLC classified imagery achieved better results as compare to MLC classified imagery without topographic correction. It is expected that this instigated paper report on different preprocessing phases of satellite imagery delivers an effective guidance to algorithm designers for accurately use the diverse and complex remotely sensed data of flat as well as mountainous or rugged surface.