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Comment published on 26 March 2016, see Remote Sens. 2016, 8(4), 288.

Open AccessArticle
Remote Sens. 2015, 7(7), 8368-8390; doi:10.3390/rs70708368

The Improvement of Land Cover Classification by Thermal Remote Sensing

1
Department of Geography, Ludwig Maximilian University of Munich, Munich 80333, Germany
2
Institute for Water Management, Hydrology and Hydraulic Engineering (IWHW), University of Natural Resources and Life Sciences, Vienna 1180, Austria
*
Author to whom correspondence should be addressed.
Academic Editors: Ruiliang Pu and Prasad S. Thenkabail
Received: 1 May 2015 / Revised: 1 June 2015 / Accepted: 16 June 2015 / Published: 26 June 2015
View Full-Text   |   Download PDF [3968 KB, uploaded 26 June 2015]   |  

Abstract

Land cover classification has been widely investigated in remote sensing for agricultural, ecological and hydrological applications. Landsat images with multispectral bands are commonly used to study the numerous classification methods in order to improve the classification accuracy. Thermal remote sensing provides valuable information to investigate the effectiveness of the thermal bands in extracting land cover patterns. k-NN and Random Forest algorithms were applied to both the single Landsat 8 image and the time series Landsat 4/5 images for the Attert catchment in the Grand Duchy of Luxembourg, trained and validated by the ground-truth reference data considering the three level classification scheme from COoRdination of INformation on the Environment (CORINE) using the 10-fold cross validation method. The accuracy assessment showed that compared to the visible and near infrared (VIS/NIR) bands, the time series of thermal images alone can produce comparatively reliable land cover maps with the best overall accuracy of 98.7% to 99.1% for Level 1 classification and 93.9% to 96.3% for the Level 2 classification. In addition, the combination with the thermal band improves the overall accuracy by 5% and 6% for the single Landsat 8 image in Level 2 and Level 3 category and provides the best classified results with all seven bands for the time series of Landsat TM images. View Full-Text
Keywords: thermal remote sensing; land cover classification; Landsat image; k-NN; random forest; cross validation thermal remote sensing; land cover classification; Landsat image; k-NN; random forest; cross validation
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Sun, L.; Schulz, K. The Improvement of Land Cover Classification by Thermal Remote Sensing. Remote Sens. 2015, 7, 8368-8390.

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