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Home / Photogrammetric Engineering & Remote Sensing, Number 2 / February 2012

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

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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.

Document Type: Research Article

Publication date: 01 February 2012

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  • The official journal of the American Society for Photogrammetry and Remote Sensing - the Imaging and Geospatial Information Society (ASPRS). This highly respected publication covers all facets of photogrammetry and remote sensing methods and technologies.

    Founded in 1934, the American Society for Photogrammetry and Remote Sensing (ASPRS) is a scientific association serving over 7,000 professional members around the world. Our mission is to advance knowledge and improve understanding of mapping sciences to promote the responsible applications of photogrammetry, remote sensing, geographic information systems (GIS), and supporting technologies.
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