2018
DOI: 10.5194/essd-10-219-2018
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Gross and net land cover changes in the main plant functional types derived from the annual ESA CCI land cover maps (1992–2015)

Abstract: Abstract. Land-use and land-cover change (LULCC) impacts local energy and water balance and contributes on global scale to a net carbon emission to the atmosphere. The newly released annual ESA CCI (climate change initiative) land cover maps provide continuous land cover changes at 300 m resolution from 1992 to 2015, and can be used in land surface models (LSMs) to simulate LULCC effects on carbon stocks and on surface energy budgets. Here we investigate the absolute areas and gross and net changes in differen… Show more

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Cited by 226 publications
(142 citation statements)
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“…This is a widely cited conjecture to explain the mismatch in seasonality of fire CO emissions between bottom‐up and top‐down estimates (van der Werf et al, ). We use two independent land cover maps from Li et al () and Hansen et al () for comparison, and we also use two different burned area products from GFED 4.1 s and MERIS 4.1 (Pettinari et al, ), respectively.…”
Section: Methods and Datamentioning
confidence: 99%
“…This is a widely cited conjecture to explain the mismatch in seasonality of fire CO emissions between bottom‐up and top‐down estimates (van der Werf et al, ). We use two independent land cover maps from Li et al () and Hansen et al () for comparison, and we also use two different burned area products from GFED 4.1 s and MERIS 4.1 (Pettinari et al, ), respectively.…”
Section: Methods and Datamentioning
confidence: 99%
“…Human population density (PD) was taken from the global gridded data set of changes (Pesaresi et al 2013). Land cover was taken from the Land cover_CCI dataset (version 2.0.7, 1992-2015) (Li et al 2018). We translated land cover classes to the fractional coverage of plant functional types (PFT) per 0.25°grid cells using the cross-walking approach (Poulter et al 2015, Li et al 2016 with the conversion factors and PFT definitions as in Forkel et al (2017).…”
Section: Predictor Datamentioning
confidence: 99%
“…The LUC data used in this study were from the Climate Change Initiative Land Cover dataset (CCI_LC) (https://www.esa-landcover-cci.org/), which has an annual interval and a spatial resolution of 300 m. CCI_LC maps were produced by the European Space Agency based on AVHRR HRPT, SPOT‐Vegetation, and PROBA‐V remote sensing images, using the UN Land Cover Classification System as a classification system and containing 22 land cover types (Li, MacBean et al, ). We collected the CCI_LC data of MHRB in 2000 and 2014.…”
Section: Methodsmentioning
confidence: 99%