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Remote Sens. 2016, 8(1), 70; doi:10.3390/rs8010070

A Review of the Application of Optical and Radar Remote Sensing Data Fusion to Land Use Mapping and Monitoring

1
Department of GeoSciences and Natural Resource Management, University of Copenhagen, Copenhagen 1165, Denmark
2
Geography Department, Humboldt-University Berlin, Unter den Linden 6, Berlin 10099, Germany
3
Integrative Research Institute on Transformations of Human-Environment Systems (IRI THESys), Humboldt-University Berlin, Unter den Linden 6, Berlin 10099, Germany
4
F.R.S.-FNRS, Brussels 1000, Belgium
5
Earth and Life Institute (ELI), Université Catholique de Louvain, Louvain-La-Neuve 1348, Belgium
6
School of GeoSciences, University of Edinburgh, Edinburgh EH9 3FF, UK
7
Laboratory of Geo-Information Science and Remote Sensing, Wageningen University, Droevendaalsesteeg 3, Wageningen 6708 PB, The Netherlands
8
Institute of Geographical Sciences, Freie Universität Berlin, Malteserstr. 74 - 100, Berlin 12249, Germany
These authors contributed equally to this work.
*
Author to whom correspondence should be addressed.
Academic Editors: Linda See, Martin Herold, Magaly Koch and Prasad S. Thenkabail
Received: 21 October 2015 / Revised: 5 January 2016 / Accepted: 8 January 2016 / Published: 16 January 2016
(This article belongs to the Special Issue Validation and Inter-Comparison of Land Cover and Land Use Data)
View Full-Text   |   Download PDF [2647 KB, uploaded 16 January 2016]   |  

Abstract

The wealth of complementary data available from remote sensing missions can hugely aid efforts towards accurately determining land use and quantifying subtle changes in land use management or intensity. This study reviewed 112 studies on fusing optical and radar data, which offer unique spectral and structural information, for land cover and use assessments. Contrary to our expectations, only 50 studies specifically addressed land use, and five assessed land use changes, while the majority addressed land cover. The advantages of fusion for land use analysis were assessed in 32 studies, and a large majority (28 studies) concluded that fusion improved results compared to using single data sources. Study sites were small, frequently 300–3000 km 2 or individual plots, with a lack of comparison of results and accuracies across sites. Although a variety of fusion techniques were used, pre-classification fusion followed by pixel-level inputs in traditional classification algorithms (e.g., Gaussian maximum likelihood classification) was common, but often without a concrete rationale on the applicability of the method to the land use theme being studied. Progress in this field of research requires the development of robust techniques of fusion to map the intricacies of land uses and changes therein and systematic procedures to assess the benefits of fusion over larger spatial scales. View Full-Text
Keywords: optical; synthetic aperture radar; meta-analysis; Landsat; ALOS PALSAR; ERS-1 and -2; land cover; decision tree; machine learning; pixel- and segment-level analyses optical; synthetic aperture radar; meta-analysis; Landsat; ALOS PALSAR; ERS-1 and -2; land cover; decision tree; machine learning; pixel- and segment-level analyses
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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

Joshi, N.; Baumann, M.; Ehammer, A.; Fensholt, R.; Grogan, K.; Hostert, P.; Jepsen, M.R.; Kuemmerle, T.; Meyfroidt, P.; Mitchard, E.T.A.; Reiche, J.; Ryan, C.M.; Waske, B. A Review of the Application of Optical and Radar Remote Sensing Data Fusion to Land Use Mapping and Monitoring. Remote Sens. 2016, 8, 70.

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