Classifying Land Cover Using Spectral Signature

Studying land cover has become increasingly important as countries try to overcome the destruction of wetlands; its impact on local climate due to seasonal variation, radiation balance, and deteriorating environmental quality. In this investigation, we have been studying the spectral signatures of the Jamaica Bay wetland area based on remotely sensed satellite input data from LANDSAT TM and ASTER. We applied various remote sensing techniques to generate classified land cover output maps. Our classifiers relied on input from both the remote sensing and in-situ spectral field data. Based upon spectral separability and data collected in the field, a supervised and unsupervised classification was carried out. First results suggest good agreement between the land cover units mapped and those observed in the field.