全球智库信息集成服务系统(G2TT): Development of a global hybrid forest mask through the synergy of remote sensing, crowdsourcing and FAO statistics.

G2TT

来源类型	Article
规范类型	其他
DOI	10.1016/j.rse.2015.02.011
	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
发表日期	2015
出处	Remote Sensing of Environment 162: 208-220
出版年	2015
语种	英语
摘要	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.
主题	Ecosystems Services and Management (ESM)
关键词	forest cover geographically weighted regression crowdsourcing
URL	http://pure.iiasa.ac.at/id/eprint/11491/
来源智库	International Institute for Applied Systems Analysis (Austria)
引用统计
资源类型	智库出版物
条目标识符	http://119.78.100.153/handle/2XGU8XDN/130390
推荐引用方式 GB/T 7714	Schepaschenko D,See L,Lesiv M,et al. Development of a global hybrid forest mask through the synergy of remote sensing, crowdsourcing and FAO statistics.. 2015.