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Remote Sens. 2016, 8(3), 261; doi:10.3390/rs8030261

Comparison of Data Fusion Methods Using Crowdsourced Data in Creating a Hybrid Forest Cover Map

1
International Institute for Applied Systems Analysis, Schlossplatz 1, Laxenburg A-2361, Austria
2
School of Mathematics and Statistics, University of Canterbury, Private Bag 4800, Christchurch 8140, New Zealand
3
Moscow State Forest University, Institutskaya 1, Mytischi 141005, Russia
4
School of Geography, University of Leeds, Woodhouse Lane, Leeds LS2 9JT, UK
*
Author to whom correspondence should be addressed.
Academic Editors: Martin Herold, Parth Sarathi Roy, Lars T. Waser and Prasad S. Thenkabail
Received: 2 December 2015 / Revised: 1 March 2016 / Accepted: 16 March 2016 / Published: 22 March 2016
(This article belongs to the Special Issue Validation and Inter-Comparison of Land Cover and Land Use Data)
View Full-Text   |   Download PDF [2950 KB, uploaded 22 March 2016]   |  

Abstract

Data fusion represents a powerful way of integrating individual sources of information to produce a better output than could be achieved by any of the individual sources on their own. This paper focuses on the data fusion of different land cover products derived from remote sensing. In the past, many different methods have been applied, without regard to their relative merit. In this study, we compared some of the most commonly-used methods to develop a hybrid forest cover map by combining available land cover/forest products and crowdsourced data on forest cover obtained through the Geo-Wiki project. The methods include: nearest neighbour, naive Bayes, logistic regression and geographically-weighted logistic regression (GWR), as well as classification and regression trees (CART). We ran the comparison experiments using two data types: presence/absence of forest in a grid cell; percentage of forest cover in a grid cell. In general, there was little difference between the methods. However, GWR was found to perform better than the other tested methods in areas with high disagreement between the inputs. View Full-Text
Keywords: data fusion methods; forest map; remote sensing; geographically-weighted regression data fusion methods; forest map; remote sensing; geographically-weighted regression
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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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Lesiv, M.; Moltchanova, E.; Schepaschenko, D.; See, L.; Shvidenko, A.; Comber, A.; Fritz, S. Comparison of Data Fusion Methods Using Crowdsourced Data in Creating a Hybrid Forest Cover Map. Remote Sens. 2016, 8, 261.

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