Global Land Cover Map for 2009 (GlobCover 2009)

Arino, Olivier; Perez, Jose Julio Ramos; Kalogirou, Vasileios; Bontemps, Sophie; Defourny, Pierre; Bogaert, Eric Van

doi:10.1594/pangaea.787668

Cited by 75 publications

(13 citation statements)

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“…The overall effect on strength can be variable and highly dependent on localized properties. We therefore test land cover data in our model as a proxy for (Arino et al, 2012) and separated into 20 classes; and (4) a data set that represents the maximum green vegetation fraction (0-100%) in an area and is based on MODIS-derived normalized difference vegetation index data from 2001 to 2012, available at 30 arc sec (~1-km) resolution (Broxton, Zeng, Scheftic, & Troch, 2014).…”

Section: Independent Variablesmentioning

confidence: 99%

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A Global Empirical Model for Near‐Real‐Time Assessment of Seismically Induced Landslides

et al. 2018

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Earthquake‐triggered landslides are a significant hazard in seismically active regions, but our ability to assess the hazard they pose in near‐real‐time is limited. In this study, we present a new globally applicable model for seismically induced landslides based on the most comprehensive global data set available; we use 23 landslide inventories that span a range of earthquake magnitudes and climatic and tectonic settings. We use logistic regression to relate the presence and distribution of earthquake‐triggered landslides with spatially distributed estimates of ground shaking, topographic slope, lithology, land cover type, and a topographic index designed to estimate variability in soil wetness to provide an empirical model of landslide distribution. We tested over 100 combinations of independent predictor variables to find the best fitting model, using a diverse set of statistical tests. Blind validation tests show that the model accurately estimates the distribution of available landslide inventories. The results indicate that the model is reliable and stable, with high balanced accuracy (correctly versus incorrectly classified pixels) for the majority of test events. A cross‐validation analysis shows high balanced accuracy for a majority of events as well. By combining near‐real‐time estimates of ground shaking with globally available landslide susceptibility data, this model provides a tool to estimate the distribution of coseismic landslide hazard within minutes of the occurrence of any earthquake worldwide for which a U.S. Geological Survey ShakeMap is available.

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Section: Independent Variablesmentioning

confidence: 99%

“…Mean monthly precipitation data are from Hijmans et al (2005). Global land cover data for 2009 (GlobCover) are from Arino et al (2012). MODIS land cover data are from Broxton, Zeng, Sulla-Menashe, and Troch (2014).…”

Section: Acknowledgmentsmentioning

confidence: 99%

A Global Empirical Model for Near‐Real‐Time Assessment of Seismically Induced Landslides

et al. 2018

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“…Besides, there is a distinct difference in the spatial pattern of cropland in North America and Africa, which is likely attributable to their inputs. For example, the cropland quality in ESA CCI is notably higher than Global Land Cover Map for 2009 (GlobCover 2009) 62 used in Venter et al . 13 regarding their spatial patterns and the temporal dynamics.…”

Section: Technical Validationmentioning

confidence: 95%

A global record of annual terrestrial Human Footprint dataset from 2000 to 2018

Wen

et al. 2022

Sci Data

152

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Human Footprint, the pressure imposed on the eco-environment by changing ecological processes and natural landscapes, is raising worldwide concerns on biodiversity and ecological conservation. Due to the lack of spatiotemporally consistent datasets of Human Footprint over a long temporal span, many relevant studies on this topic have been limited. Here, we mapped the annual dynamics of the global Human Footprint from 2000 to 2018 using eight variables that reflect different aspects of human pressures. The accuracy assessment revealed a good agreement between our mapped results and the previously developed datasets in different years. We found more than two million km2 of wilderness (i.e., regions with Human Footprint values below one) were lost over the past two decades. The biome dominated by mangroves experienced the most significant loss (i.e., above 5%) of wilderness, likely attributed to intensified human activities in coastal areas. The derived annual and spatiotemporally consistent global Human Footprint can be a fundamental dataset for many relevant studies about human activities and natural resources.

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“…The values of elevation were the original values of DEM, whereas the values of slope and aspect were calculated using the slope-aspect function of ArcGIS. Elevation and slope were classified into four classes in order to compare the variations of LSI and VOL across the Second, the used land cover data were Globcover 2009 V2.3 from European Space Agency (http://due.esrin.esa.int/page_globcover.php; accessed on 20 February 2021) with a resolution of 300 m [67]. In these data, 23 types of land cover are developed.…”

Section: Data Collectionmentioning

confidence: 99%

Influence of Earthquakes on Landslide Susceptibility in a Seismic Prone Catchment in Central Asia

Torgoev

Zaredinov

et al. 2021

Applied Sciences

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Central Asia is one of the most challenged places, prone to suffering from various natural hazards, where seismically triggered landslides have caused severe secondary losses. Research on this problem is especially important in the cross-border Mailuu-Suu catchment in Kyrgyzstan, since it is burdened by radioactive legacy sites and frequently affected by earthquakes and landslides. To identify the landslide-prone areas and to quantify the volume of landslide (VOL), Scoops3D was selected to evaluate the slope stability throughout a digital landscape in the Mailuu-Suu catchment. By performing the limit equilibrium analysis, both of landslide susceptibility index (LSI) and VOL were estimated under five earthquake scenarios. The results show that the upstream areas were more seismically vulnerable than the downstream areas. The susceptibility level rose significantly with the increase in earthquake strength, whereas the VOL was significantly higher under the extreme earthquake scenario than under the other four scenarios. After splitting the environmental variables into sub-classes, the spatial variations of LSI and VOL became more clear: the LSI reduced with the increase in elevation, slope, annual precipitation, and distances to faults, roads, and streams, whereas the highest VOL was observed in the areas with moderate elevations, high precipitation, grasslands, and mosaic vegetation. The relative importance analysis indicated that the explanatory power reduced with the increase in earthquake level and it was significant higher for LSI than for VOL. Among nine environmental variables, the distance to faults, annual precipitation, slope, and elevation were identified as important triggers of landslides. By a simultaneous assessment of both LSI and VOL and the identification of important triggers, the proposed modelling approaches can support local decision-makers and householders to identify landslide-prone areas, further design proper landslide hazard and risk management plans and, consequently, contribute to the resolution of transboundary pollution conflicts.

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Global Land Cover Map for 2009 (GlobCover 2009)

Cited by 75 publications

References 0 publications

A Global Empirical Model for Near‐Real‐Time Assessment of Seismically Induced Landslides

A Global Empirical Model for Near‐Real‐Time Assessment of Seismically Induced Landslides

A global record of annual terrestrial Human Footprint dataset from 2000 to 2018

Influence of Earthquakes on Landslide Susceptibility in a Seismic Prone Catchment in Central Asia

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