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Response to Johnson B.A. Scale Issues Related to the Accuracy Assessment of Land Use/Land Cover Maps Produced Using Multi-Resolution Data: Comments on “The Improvement of Land Cover Classification by Thermal Remote Sensing”. Remote Sens. 2015, 7, 8368–8390
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Remote Sens. 2015, 7(10), 13436-13439; doi:10.3390/rs71013436

Scale Issues Related to the Accuracy Assessment of Land Use/Land Cover Maps Produced Using Multi-Resolution Data: Comments on “The Improvement of Land Cover Classification by Thermal Remote Sensing”. Remote Sens. 2015, 7(7), 8368–8390

Institute for Global Environmental Strategies, 2108-11 Kamiyamaguchi, Hayama, Kanagawa 240-0115, Japan
Academic Editors: Ruiliang Pu and Prasad Thenkabail
Received: 2 July 2015 / Accepted: 10 October 2015 / Published: 15 October 2015
View Full-Text   |   Download PDF [178 KB, uploaded 15 October 2015]   |  

Abstract

Much remote sensing (RS) research focuses on fusing, i.e., combining, multi-resolution/multi-sensor imagery for land use/land cover (LULC) classification. In relation to this topic, Sun and Schulz [1] recently found that a combination of visible-to-near infrared (VNIR; 30 m spatial resolution) and thermal infrared (TIR; 100–120 m spatial resolution) Landsat data led to more accurate LULC classification. They also found that using multi-temporal TIR data alone for classification resulted in comparable (and in some cases higher) classification accuracies to the use of multi-temporal VNIR data, which contrasts with the findings of other recent research [2]. This discrepancy, and the generally very high LULC accuracies achieved by Sun and Schulz (up to 99.2% overall accuracy for a combined VNIR/TIR classification result), can likely be explained by their use of an accuracy assessment procedure which does not take into account the multi-resolution nature of the data. Sun and Schulz used 10-fold cross-validation for accuracy assessment, which is not necessarily inappropriate for RS accuracy assessment in general. However, here it is shown that the typical pixel-based cross-validation approach results in non-independent training and validation data sets when the lower spatial resolution TIR images are used for classification, which causes classification accuracy to be overestimated. View Full-Text
Keywords: image fusion; cross-validation; multi-resolution; multi-sensor; Landsat 8 image fusion; cross-validation; multi-resolution; multi-sensor; Landsat 8
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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Johnson, B.A. Scale Issues Related to the Accuracy Assessment of Land Use/Land Cover Maps Produced Using Multi-Resolution Data: Comments on “The Improvement of Land Cover Classification by Thermal Remote Sensing”. Remote Sens. 2015, 7(7), 8368–8390. Remote Sens. 2015, 7, 13436-13439.

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