4.7 Article

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

期刊

REMOTE SENSING
卷 8, 期 3, 页码 -

出版社

MDPI
DOI: 10.3390/rs8030261

关键词

forest map; geographically-weighted regression; data fusion methods; remote sensing

资金

  1. Marie Curie individual Grant FP7-MC-IIF: SIFCAS (Spatially integrated forest carbon accounting system) Project [627481]
  2. EU [617754]

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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.

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