Development of a global hybrid forest mask through the synergy of remote sensing, crowdsourcing and FAO statistics

Schepaschenko D, See L, Lesiv M, McCallum I, Fritz S, Salk C, Perger C, Shvidenko A, et al. (2015). Development of a global hybrid forest mask through the synergy of remote sensing, crowdsourcing and FAO statistics. Remote Sensing of Environment 162: 208-220. DOI:10.1016/j.rse.2015.02.011.

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Project: Operational Global Carbon Observing System (GEOCARBON, FP7 283080), Exploring the potential for agricultural and biomass trade in the Commonwealth of Independent States (AGRICISTRADE, FP7 612755), Stimulating Innovation for Global Monitoring of Agriculture and its Impact on the Environment in support of GEOGLAM (SIGMA, FP7 603719), Spatially integrated forest carbon accounting system (SIFCAS, FP7 627481)

Abstract

A number of global and regional maps of forest extent are available, but when compared spatially, there are large areas of disagreement. Moreover, there is currently no global forest map that is consistent with forest statistics from FAO (Food and Agriculture Organiztion of the United Nations). By combining these diverse data sources into a single forest cover product, it is possible to produce a global forest map that is more accurate than the individual input layers and to produce a map that is consistent with FAO statistics. In this paper we applied geographically weighted regression (GWR) to integrate eight different forest products into three global hybrid forest cover maps at a 1 km resolution for the reference year 2000. Input products included global land cover and forest maps at varying resolutions from 30 m to 1 km, mosaics of regional land use/land cover products where available, and the MODIS Vegetation Continuous Fields product. The GWR was trained using crowdsourced data collected via the Geo-Wiki platform and the hybrid maps were then validated using an independent dataset collected via the same system. Three different hybrid maps were produced: two consistent with FAO statistics, one at the country and one at the regional level, and a "best guess" forest cover map that is independent of FAO. Independent validation showed that the "bes guess" hybrid product had the best overall accuracy of 93% when compared with the individual input datasets. The global hybrid forest cover maps are availale at http://biomass.geo-wiki.org.

Item Type: Article
Uncontrolled Keywords: forest cover; geographically weighted regression; crowdsourcing
Research Programs: Ecosystems Services and Management (ESM)
Bibliographic Reference: Remote Sensing of Environment; 162:208-220 (June 2015) (Published online 13 March 2015)
Depositing User: IIASA Import
Date Deposited: 15 Jan 2016 08:53
Last Modified: 27 Jan 2017 11:12
URI: http://pure.iiasa.ac.at/11491

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