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Remote Sens. 2017, 9(7), 682; doi:10.3390/rs9070682

Reconstructing Historical Land Cover Type and Complexity by Synergistic Use of Landsat Multispectral Scanner and CORONA

1
Institute of Agricultural Remote Sensing and Information Technology, College of Environment and Natural Resource, Zhejiang University, Hangzhou 310058, China
2
Faculty of Science and Technology, Lancaster University, Bailrigg, Lancaster LA1 4YQ, UK
3
Department of Geography, Michigan State University, East Lansing, MI 48823, USA
4
Department of atmospheric oceanic and earth science, George Mason University, VA 22030, USA
5
Department of Earth Science, Jilin University, Changchun 130061, China
*
Authors to whom correspondence should be addressed.
Academic Editors: Chuanrong Zhang, Magaly Koch and Prasad S. Thenkabail
Received: 2 May 2017 / Revised: 15 June 2017 / Accepted: 29 June 2017 / Published: 3 July 2017
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Abstract

Survey data describing land cover information such as type and diversity over several decades are scarce. Therefore, our capacity to reconstruct historical land cover using field data and archived remotely sensed data over large areas and long periods of time is somewhat limited. This study explores the relationship between CORONA texture—a surrogate for actual land cover type and complexity—with spectral vegetation indices and texture variables derived from Landsat MSS under the Spectral Variation Hypothesis (SVH) such as to reconstruct historical continuous land cover type and complexity. Image texture of CORONA was calculated using a mean occurrence measure while image textures of Landsat MSS were calculated by occurrence and co-occurrence measures. The relationship between these variables was evaluated using correlation and regression techniques. The reconstruction procedure was undertaken through regression kriging. The results showed that, as expected, texture based on the visible bands and corresponding indices indicated larger correlation with CORONA texture, a surrogate of land cover (correlation >0.65). In terms of prediction, the combination of the first-order mean of band green, second-order measure of tasseled cap brightness, second-order mean of Normalized Visible Index (NVI) and second-order entropy of NIR yielded the best model with respect to Akaike’s Information Criterion (AIC), r-square, and variance inflation factors (VIF). The regression model was then used in regression kriging to map historical continuous land cover. The resultant maps indicated the type and degree of complexity in land cover. Moreover, the proposed methodology minimized the impacts of topographic shadow in the region. The performance of this approach was compared with two conventional classification methods: hard classifiers and continuous classifiers. In contrast to conventional techniques, the technique could clearly quantify land cover complexity and type. Future applications of CORONA datasets such as this one could include: improved quality of CORONA imagery, studies of the CORONA texture measures for extracting ecological parameters (e.g., species distributions), change detection and super resolution mapping using CORONA and Landsat MSS. View Full-Text
Keywords: historical land cover; CORONA; Landsat MSS; land cover type; land cover complexity; spectral variation hypothesis (SVH); image texture; regression kriging historical land cover; CORONA; Landsat MSS; land cover type; land cover complexity; spectral variation hypothesis (SVH); image texture; regression kriging
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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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MDPI and ACS Style

Shahtahmassebi, A.R.; Lin, Y.; Lin, L.; Atkinson, P.M.; Moore, N.; Wang, K.; He, S.; Huang, L.; Wu, J.; Shen, Z.; Gan, M.; Zheng, X.; Su, Y.; Teng, H.; Li, X.; Deng, J.; Sun, Y.; Zhao, M. Reconstructing Historical Land Cover Type and Complexity by Synergistic Use of Landsat Multispectral Scanner and CORONA. Remote Sens. 2017, 9, 682.

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