Using a Similarity Matrix Approach to Evaluate the Accuracy of Rescaled Maps

Rescaled maps have been extensively utilized to provide data at the appropriate spatial resolution for use in various Earth science models. However, a simple and easy way to evaluate these rescaled maps has not been developed. We propose a similarity matrix approach using a contingency table to comp...

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Main Authors: Peijun Sun, Russell G. Congalton
Format: Article
Language:English
Published: MDPI AG 2018-03-01
Series:Remote Sensing
Subjects:
Online Access:http://www.mdpi.com/2072-4292/10/3/487
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spelling doaj-31adf772d53d4bd9ac6094dfa60853d92020-11-25T00:24:11ZengMDPI AGRemote Sensing2072-42922018-03-0110348710.3390/rs10030487rs10030487Using a Similarity Matrix Approach to Evaluate the Accuracy of Rescaled MapsPeijun Sun0Russell G. Congalton1State Key Laboratory of Remote Sensing Science, Jointly Sponsored by Beijing Normal University and Institute of Remote Sensing and Digital Earth of Chinese Academy of Sciences, Beijing 100875, ChinaDepartment of Natural Resources & the Environment, University of New Hampshire, Durham, NH 03824, USARescaled maps have been extensively utilized to provide data at the appropriate spatial resolution for use in various Earth science models. However, a simple and easy way to evaluate these rescaled maps has not been developed. We propose a similarity matrix approach using a contingency table to compute three measures: overall similarity (OS), omission error (OE), and commission error (CE) to evaluate the rescaled maps. The Majority Rule Based aggregation (MRB) method was employed to produce the upscaled maps to demonstrate this approach. In addition, previously created, coarser resolution land cover maps from other research projects were also available for comparison. The question of which is better, a map initially produced at coarse resolution or a fine resolution map rescaled to a coarse resolution, has not been quantitatively investigated. To address these issues, we selected study sites at three different extent levels. First, we selected twelve regions covering the continental USA, then we selected nine states (from the whole continental USA), and finally we selected nine Agriculture Statistical Districts (ASDs) (from within the nine selected states) as study sites. Crop/non-crop maps derived from the USDA Crop Data Layer (CDL) at 30 m as base maps were used for the upscaling and existing maps at 250 m and 1 km were utilized for the comparison. The results showed that a similarity matrix can effectively provide the map user with the information needed to assess the rescaling. Additionally, the upscaled maps can provide higher accuracy and better represent landscape pattern compared to the existing coarser maps. Therefore, we strongly recommend that an evaluation of the upscaled map and the existing coarser resolution map using a similarity matrix should be conducted before deciding which dataset to use for the modelling. Overall, extending our understanding on how to perform an evaluation of the rescaled map and investigation of the applicability of the rescaled map compared to the existing land cover map is necessary for users to most effectively use these data in Earth science models.http://www.mdpi.com/2072-4292/10/3/487similarity matrixaccuracy assessmentrescaling techniqueland cover mapupscaled mapheterogeneity
collection DOAJ
language English
format Article
sources DOAJ
author Peijun Sun
Russell G. Congalton
spellingShingle Peijun Sun
Russell G. Congalton
Using a Similarity Matrix Approach to Evaluate the Accuracy of Rescaled Maps
Remote Sensing
similarity matrix
accuracy assessment
rescaling technique
land cover map
upscaled map
heterogeneity
author_facet Peijun Sun
Russell G. Congalton
author_sort Peijun Sun
title Using a Similarity Matrix Approach to Evaluate the Accuracy of Rescaled Maps
title_short Using a Similarity Matrix Approach to Evaluate the Accuracy of Rescaled Maps
title_full Using a Similarity Matrix Approach to Evaluate the Accuracy of Rescaled Maps
title_fullStr Using a Similarity Matrix Approach to Evaluate the Accuracy of Rescaled Maps
title_full_unstemmed Using a Similarity Matrix Approach to Evaluate the Accuracy of Rescaled Maps
title_sort using a similarity matrix approach to evaluate the accuracy of rescaled maps
publisher MDPI AG
series Remote Sensing
issn 2072-4292
publishDate 2018-03-01
description Rescaled maps have been extensively utilized to provide data at the appropriate spatial resolution for use in various Earth science models. However, a simple and easy way to evaluate these rescaled maps has not been developed. We propose a similarity matrix approach using a contingency table to compute three measures: overall similarity (OS), omission error (OE), and commission error (CE) to evaluate the rescaled maps. The Majority Rule Based aggregation (MRB) method was employed to produce the upscaled maps to demonstrate this approach. In addition, previously created, coarser resolution land cover maps from other research projects were also available for comparison. The question of which is better, a map initially produced at coarse resolution or a fine resolution map rescaled to a coarse resolution, has not been quantitatively investigated. To address these issues, we selected study sites at three different extent levels. First, we selected twelve regions covering the continental USA, then we selected nine states (from the whole continental USA), and finally we selected nine Agriculture Statistical Districts (ASDs) (from within the nine selected states) as study sites. Crop/non-crop maps derived from the USDA Crop Data Layer (CDL) at 30 m as base maps were used for the upscaling and existing maps at 250 m and 1 km were utilized for the comparison. The results showed that a similarity matrix can effectively provide the map user with the information needed to assess the rescaling. Additionally, the upscaled maps can provide higher accuracy and better represent landscape pattern compared to the existing coarser maps. Therefore, we strongly recommend that an evaluation of the upscaled map and the existing coarser resolution map using a similarity matrix should be conducted before deciding which dataset to use for the modelling. Overall, extending our understanding on how to perform an evaluation of the rescaled map and investigation of the applicability of the rescaled map compared to the existing land cover map is necessary for users to most effectively use these data in Earth science models.
topic similarity matrix
accuracy assessment
rescaling technique
land cover map
upscaled map
heterogeneity
url http://www.mdpi.com/2072-4292/10/3/487
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