Highlighting continued uncertainty in global land cover maps for the user community

By Steffen Fritz, Linda M. See, I. Mccallum, C. Schill, Michael Obersteiner, M. van der Velde, H. Boettcher, Petr Havlík, Frédéric Achard. 2011

230 citations recorded by the source corpus.

In the last 10 years a number of new global datasets have been created and new, more sophisticated algorithms have been designed to classify land cover. GlobCover and MODIS v.5 are the most recent global land cover products available, where GlobCover (300 m) has the finest spatial resolution of other comparable products such as MODIS v.5 (500 m) and GLC-2000 (1 km). This letter shows that the thematic accuracy in the cropland domain has decreased when comparing these two latest products. This disagreement is also evident spatially when examining maps of cropland and forest disagreement between GLC-2000, MODIS and GlobCover. The analysis highlights the continued uncertainty surrounding these products, with a combined forest and cropland disagreement of 893 Mha (GlobCover versus MODIS v.5). This letter suggests that data sharing efforts and the provision of more in situ data for training, calibration and validation are very important conditions for improving future global land cover products.

Subfields: Ecology, Global and Planetary Change, Media Technology

View Paper: https://www.semanticscholar.org/paper/f49299090dd23502a2affc4688a61f5a87aa715d

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Citations in the corpus

Geo-Wiki: An online platform for improving global land cover (2012, methodology): “A subset of these contributions was used to validate a hybrid croplandmap in Africa (Fritz et al., 2011c), which can be viewed and downloaded from agriculture.geo-wiki.org.”
Global, 30-m resolution continuous fields of tree cover: Landsat-based rescaling of MODIS vegetation continuous fields with lidar-based estimates of error (2013, background): “Challenges to global land cover mapping at coarse resolution have been summarized by a number of review papers (e.g. Townshend et al. 1994; Cihlar 2000; Fritz et al 2011), and issues surrounding global land cover monitoring at Landsat-class resolution are reviewed by Townshend et al. (2012).”
Mapping cropland-use intensity across Europe using MODIS NDVI time series (2016, background): “Unfortunately, existing data on cropland-use intensity are mostly coarse in scale, heavily rely on uncertain croplandmaps (Fritz et al 2011, Fritz et al 2013), or are based on national statistics, which themselves may contain uncertainties (Verburg et al 2011, Zaks and Kucharik 2011).”
Automated cropland mapping of continental Africa using Google Earth Engine cloud computing (2017, methodology): “For example, Cropland estimates derived from GlobCover are 20% higher than those derived from MODIS globally (Fritz et al., 2011a,b).”
Scarcity amidst abundance? Reassessing the potential for cropland expansion in Africa (2014, background): “Despite the rapid expansion of satellite imagery and other remote sensing information in recent years, there remains a lack of consistent and reliable data on the location and area intensity of land cultivation (Fritz et al., 2011).”
A Pixel-Based Landsat Compositing Algorithm for Large Area Land Cover Mapping (2013, unknown): “land cover products [15], [40].”