Comparing the Radiometric Behavior of LISS III Data of IRS 1D and IRS P6 for Landscape Ecological Application

Abstract

ABSTRACT. In the recent time, the usage of multi date satellite remote sensing data is a commonest practice to monitor and model the landscape dynamics. But the reliability of information obtained from such time series analysis of satellite imagery depends heavily on the coherence in the spectral behavior of the multi-dates satellite data. The inconsistency in the spectral behavior among the multidate satellite data may be attributed to differences in satellite orbit and sensor characteristics and even the environmental conditions. Indian Remote Sensing satellite (IRS) 1D and IRS P6 have been planned to have sensors of almost identical specifications to cater continued data service and to facilitate temporal analysis for resource planning and management. In the present paper an attempt has been made to compare the data quality of the Linear Imaging Self scanning System III (LISS III) data from IRS 1D and IRS P6 satellite of identical characteristics and to ascertain their usability for temporal analysis. The radiometric characteristics of the LISS III images of the closest dates from IRS 1D and IRS P-6 satellites were compared band wise. Spectral classes obtained through unsupervised clustering were used to compute several class level and landscape level metrics. Statistical analysis of radiometric and landscape characteristics revealed a significant difference in the investigated dataset and their usability for temporal analysis is subjected to proper calibration of radiometric behavior of the two data sources.

Using HSPF to Model the Hydrologic and Water Quality Impacts of Riparian Land-Use Change in a Small Watershed

“…The satellite data used in this study were obtained from the sensors IRS-1D LISS III, Landsat-TM and Landsat-MSS. The LISS-III sensor of the Indian Remote Sensing satellite (IRS) has 4 bands with a spatial resolution of 23.5 m, Landsat-TM has 7 bands with a spatial resolution of 30 m and MSS has 4 bands with a spatial resolution of 80 m. Indian remote sensing satellites for the period 1999-2000 have been utilised in this research as IRS data have been successfully used for various ecological applications (Sharma, 2009). Other ancillary data used were: A hybrid classification scheme is adopted in this research for preparing the land cover map, in which a particular class is identified, isolated and extracted using the best possible procedure (either supervised or unsupervised), and finally the individual classes extracted were integrated.…”