Incorporating the Downscaled Landsat TM Thermal Band in Land-cover Classification using Random Forest.


Thermal information is a key parameter in numerous remote sensing applications and environmental studies. The aim of this study was to assess the improvement that incorporating the TIR band of the Landsat-5 TM sensor has in the classification of a large heterogeneous landscape located in the south of Spain. To incorporate the thermal data into the classification process, the TIR band (with spatial resolution of 120 m) was downscaled by means of a geostatistical method (Downscaling Cokriging) to achieve a spatial resolution of 30 meters. Then, the thermal information was evaluated for contribution to overall and per-class map accuracy using Random Forest classification. The addition of the TIR band to single-season and multi-seasonal Random Forest models leads to an increase in the overall accuracy of 10 percent and 5 percent, and to an increase in the kappa index of 10 percent and 5 percent, respectively. The increase in per-class kappa for the thermal, single-season, Random Forest model ranged from −3 percent to 47 percent and 0 percent to 12 percent for the thermal, multi-seasonal model.


Natural Resources and the Environment

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Photogrammetric Engineering and Remote Sensing


American Society for Photogrammetry and Remote Sensing

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© 2012 American Society for Photogrammetry and Remote Sensing