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

Lesiv, M. ORCID:, Moltchanova, E., Shchepashchenko, D. ORCID:, See, L. ORCID:, Shvidenko, A., Comber, A., & Fritz, S. ORCID: (2016). Comparison of Data Fusion Methods Using Crowdsourced Data in Creating a Hybrid Forest Cover Map. Remote Sensing 8 (3) e261. 10.3390/rs8030261.

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Project: Harnessing the power of crowdsourcing to improve land cover and land-use information (CROWDLAND, FP7 617754), Geo-Wiki


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.

Item Type: Article
Additional Information: This article belongs to the Special Issue "Validation and Inter-Comparison of Land Cover and Land Use Data" available here:
Uncontrolled Keywords: data fusion methods; forest map; remote sensing; geographically-weighted regression; Geo-Wiki
Research Programs: Ecosystems Services and Management (ESM)
Depositing User: Michaela Rossini
Date Deposited: 22 Mar 2016 12:16
Last Modified: 04 Jan 2024 13:53

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