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Comment of Remote Sens. 2015, 7(7), 8368-8390.

Open AccessComment
Remote Sens. 2016, 8(4), 288; doi:10.3390/rs8040288

We Must all Pay More Attention to Rigor in Accuracy Assessment: Additional Comment to “The Improvement of Land Cover Classification by Thermal Remote Sensing”. Remote Sens. 2015, 7, 8368–8390

Natural Resources Canada, Canadian Forest Service, Northern Forestry Centre, 5320 122 St, Edmonton, AB T6H 3S5, Canada
Academic Editors: Giles Foody, Norman Kerle and Prasad S. Thenkabail
Received: 14 January 2016 / Revised: 11 February 2016 / Accepted: 15 March 2016 / Published: 26 March 2016
View Full-Text   |   Download PDF [715 KB, uploaded 6 April 2016]   |  

Abstract

Despite recent calls for statistically robust and transparent accuracy assessment [1], further attention to rigor is still needed. Here I take the opportunity of a disputed accuracy assessment recently published in Remote Sensing [2,3,4] to highlight some issues regarding sampling design, response design and analysis that I often find as a reviewer, and that I too have neglected in the past, among them: (i) use of a sampling design that is purposive instead of probability based; (ii) use of suboptimal label allocation procedures for reference data; and (iii) incorrect computation of accuracy estimates and lack of reporting of confidence intervals. Following the good practices in [1] will enhance the scientific credibility of our discipline. View Full-Text
Keywords: land cover mapping; accuracy assessment; statistical rigor land cover mapping; accuracy assessment; statistical rigor
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© 2016 by the Her Majesty the Queen in Right of Canada, as represented by Natural Resources Canada; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons by Attribution (CC-BY) license (http://creativecommons.org/licenses/by/4.0/) (Canadian government papers).

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Castilla, G. We Must all Pay More Attention to Rigor in Accuracy Assessment: Additional Comment to “The Improvement of Land Cover Classification by Thermal Remote Sensing”. Remote Sens. 2015, 7, 8368–8390. Remote Sens. 2016, 8, 288.

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