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Fritz S.,International Institute For Applied Systems Analysis | See L.,International Institute For Applied Systems Analysis | Mccallum I.,International Institute For Applied Systems Analysis | You L.,Chinese Academy of Agricultural Sciences | And 47 more authors.
Global Change Biology | Year: 2015

A new 1 km global IIASA-IFPRI cropland percentage map for the baseline year 2005 has been developed which integrates a number of individual cropland maps at global to regional to national scales. The individual map products include existing global land cover maps such as GlobCover 2005 and MODIS v.5, regional maps such as AFRICOVER and national maps from mapping agencies and other organizations. The different products are ranked at the national level using crowdsourced data from Geo-Wiki to create a map that reflects the likelihood of cropland. Calibration with national and subnational crop statistics was then undertaken to distribute the cropland within each country and subnational unit. The new IIASA-IFPRI cropland product has been validated using very high-resolution satellite imagery via Geo-Wiki and has an overall accuracy of 82.4%. It has also been compared with the EarthStat cropland product and shows a lower root mean square error on an independent data set collected from Geo-Wiki. The first ever global field size map was produced at the same resolution as the IIASA-IFPRI cropland map based on interpolation of field size data collected via a Geo-Wiki crowdsourcing campaign. A validation exercise of the global field size map revealed satisfactory agreement with control data, particularly given the relatively modest size of the field size data set used to create the map. Both are critical inputs to global agricultural monitoring in the frame of GEOGLAM and will serve the global land modelling and integrated assessment community, in particular for improving land use models that require baseline cropland information. These products are freely available for downloading from the http://cropland.geo-wiki.org website. © 2015 John Wiley & Sons Ltd.

See L.,International Institute For Applied Systems Analysis | Schepaschenko D.,International Institute For Applied Systems Analysis | Lesiv M.,Lviv Polytechnic | Lesiv M.,Polish Academy of Sciences | And 23 more authors.
ISPRS Journal of Photogrammetry and Remote Sensing | Year: 2015

Land cover is of fundamental importance to many environmental applications and serves as critical baseline information for many large scale models e.g. in developing future scenarios of land use and climate change. Although there is an ongoing movement towards the development of higher resolution global land cover maps, medium resolution land cover products (e.g. GLC2000 and MODIS) are still very useful for modelling and assessment purposes. However, the current land cover products are not accurate enough for many applications so we need to develop approaches that can take existing land covers maps and produce a better overall product in a hybrid approach. This paper uses geographically weighted regression (GWR) and crowdsourced validation data from Geo-Wiki to create two hybrid global land cover maps that use medium resolution land cover products as an input. Two different methods were used: (a) the GWR was used to determine the best land cover product at each location; (b) the GWR was only used to determine the best land cover at those locations where all three land cover maps disagree, using the agreement of the land cover maps to determine land cover at the other cells. The results show that the hybrid land cover map developed using the first method resulted in a lower overall disagreement than the individual global land cover maps. The hybrid map produced by the second method was also better when compared to the GLC2000 and GlobCover but worse or similar in performance to the MODIS land cover product depending upon the metrics considered. The reason for this may be due to the use of the GLC2000 in the development of GlobCover, which may have resulted in areas where both maps agree with one another but not with MODIS, and where MODIS may in fact better represent land cover in those situations. These results serve to demonstrate that spatial analysis methods can be used to improve medium resolution global land cover information with existing products. © 2014 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS).

Schepaschenko D.G.,International Institute For Applied Systems Analysis | Schepaschenko D.G.,Moscow State Forest University | Shvidenko A.Z.,International Institute For Applied Systems Analysis | Shvidenko A.Z.,Russian Academy of Sciences | And 5 more authors.
Contemporary Problems of Ecology | Year: 2015

We review up-to-date, open access remote sensing (RS) products related to forest. We created a hybrid forest/non-forest map using geographically weighted regression (GWR) based on a number of recent RS products and crowdsourcing. The hybrid map has spatial resolution of 230 m and shows the extent of forest in Russia in 2010. We estimate area of Russian forest as 711 million ha (in accordance with Russian national forest definition). Compared to official data of the State Forest Register (SFR), RS estimates the area of forest to be considerably larger in European part (+12.2 million ha or +8%) and smaller in Asian (–39.8 million ha or–7%) part of Russia. We report the changing forest area in 2001–2010 and discuss main drivers: wildfire and encroachment of abandoned arable land. The methodology used here can by applied for monitoring of forest cover and enhancing the forest accounting system in Russia. © 2015, Pleiades Publishing, Ltd.

Schepaschenko D.,International Institute For Applied Systems Analysis | Schepaschenko D.,Moscow State Forest University | See L.,International Institute For Applied Systems Analysis | Lesiv M.,International Institute For Applied Systems Analysis | And 21 more authors.
Remote Sensing of Environment | Year: 2015

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 Organization 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 "best guess" hybrid product had the best overall accuracy of 93% when compared with the individual input datasets. The global hybrid forest cover maps are available at http://biomass.geo-wiki.org. © 2015 Elsevier Inc.

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