AntarcticaLC2000: The new Antarctic land cover database for the year 2000

Hui, Fengming; Kang, Jiesheng; Liu, Yan; Cheng, Xiao; Gong, Peng; Wang, Fang; Zhan, Li; Ye, Yufang; Guo, Zhifang

doi:10.1007/s11430-016-0029-2

Cited by 22 publications

(22 citation statements)

References 46 publications

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“…Areas of permanent snow where identified from consensus land-cover maps (Tuanmu & Jetz, 2014), glacier inventories (Raup et al, 2007;NSIDC, 2005NSIDC, -2018 and the Antarctic Land Cover map for 2000 (Hui et al, 2017). A composite map was created at 30 arc seconds spatial resolution in geographic projection, occurrences were then aggregated to half degree spatial resolution and reclassified as major occurrences (cells with > 22% snow coverage) and minor occurrences (cells with at least one occurrence).…”

Section: T61mentioning

confidence: 99%

“…Known locations of prominent ice-free rock in glacial and alpine environments were selected from global geographical gazeteers (GeoNames, 2020), glacier inventories (Raup et al 2007;NSIDC, 2005NSIDC, -2018 and the Antarctic Land Cover map for 2000 (Hui et al, 2017). Further areas with mixed occurrence of barren and snow/ice cover were identified from the Circumpolar Arctic Vegetation Map (Raynolds et al, 2019), the USGS EROS LandCover GLCCDB, version 2 (Loveland et al, 2000) and a 1-km consensus land-cover map (Tuanmu & Jetz, 2014).…”

Section: T62mentioning

confidence: 99%

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IUCN Global Ecosystem Typology 2.0: descriptive profiles for biomes and ecosystem functional groups

Kingsford¹,

Catford²,

Rains³

et al. 2020

110

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Ecosystems are critically important components of Earth’s biological diversity and as the natural capital that sustains human life and well-being. Yet all of the world’s ecosystems show hallmarks of human influence, and many are under acute risks of collapse, with consequences for habitats of species, genetic diversity, ecosystem services, sustainable development and human well-being. The IUCN Global Ecosystem Typology is a hierarchical classification system that, in its upper levels, defines ecosystems by their convergent ecological functions and, in its lower levels, distinguishes ecosystems with contrasting assemblages of species engaged in those functions. This report describes the three upper levels of the hierarchy, which provide a framework for understanding and comparing the key ecological traits of functionally different ecosystems and their drivers. An understanding of these traits and drivers is essential to support ecosystem management.

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Section: T61mentioning

confidence: 99%

Section: T62mentioning

confidence: 99%

IUCN Global Ecosystem Typology 2.0: descriptive profiles for biomes and ecosystem functional groups

Kingsford¹,

Catford²,

Rains³

et al. 2020

110

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“…There are five typical land covers over the Antarctica ice sheet and the surrounding ocean: snow and land ice, rock, sea ice, ocean water, and icebergs. Snow and land ice covers most of the Antarctic ice sheet [7,32,33], and the surface layer can be penetrated to some extent [34] by Sentinel-1A (C-band SAR). The emitted radar signal is usually backscattered from some internal layer of snow.…”

Section: Backscatter Characteristics Of Antarctic Land Surfacesmentioning

confidence: 99%

“…Therefore, the backscattered radar signal from snow and land ice is determined by dielectric properties, snow grain size, the number of internal reflectors, and the wetness of snow [35,36]. Rock takes only about 0.5 percent of the Antarctica ice sheet [32], but the backscattered radar signal from rock is complicated, depending on the corner reflectors and the surface facing the sensor. Icebergs are calved from land ice or ice shelves, and usually have a flat surface when drifting in the ocean.…”

Section: Backscatter Characteristics Of Antarctic Land Surfacesmentioning

confidence: 99%

An Automatic Method for Black Margin Elimination of Sentinel-1A Images over Antarctica

Wang¹,

Holland

2020

Remote Sensing

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The Sentinel-1A satellite was launched in April 2014 with a primary C-Band terrain observation with progressive scans synthetic aperture radar (TOPSAR) onboard and has collected plenty of high-quality images for global change studies. However, low magnitude signals around image margins (black margins) does not preserve the normal standard level, influencing the potential usage with these data. Through image analysis, we find that the signal from black margin (BM) is highly dominated by the closest effective signals and the signal in BM shows an increasing trend along the direction from image boundary to image center. An edge detector is developed based on the signal characteristics of BM. Furthermore, an automatic method to discriminate and eliminate BM is designed. Images from both extra wide (EW) and interferometric wide (IW) swath observation modes, covering the land, ocean, and coast of the Antarctic, are taken to verify the robustness of our method. Through comparison with BM edges extracted by human interpretation, our method has the maximum BM edge extraction error of 1.9 ± 3.2 pixels. When considering perimeter (or area) difference along radial direction of BM edge, our method has an averaging extraction accuracy of −0.35 ± 0.11 (or 0.14 ± 1.38) pixels, which suggests that our method is effective and can be potentially used to eliminate BM for multidisciplinary applications of Sentinel-1 data.

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“…There are four steps to finish the mosaic. Firstly, the DN values of the images were converted to TOA (top of atmosphere) spectral planetary reflectance [8]. Secondly, the spectral planetary reflectance was used as an approximation of the surface reflectance, since the concentration of atmospheric water vapor and aerosols are very low over Arctic regions [7].…”

mentioning

confidence: 99%