Field reflectance spectroscopy of sparse vegetation cover on the Antarctic Peninsula

2009 ◽  
Author(s):  
C. Haselwimmer ◽  
P. Fretwell
Keyword(s):  
Antarctic Peninsula ◽  
Vegetation Cover ◽  
Reflectance Spectroscopy ◽  
Sparse Vegetation ◽  
Field Reflectance ◽  
The Antarctic

scholarly journals Open ocean and coastal new particle formation from sulfuric acid and amines around the Antarctic Peninsula

2021 ◽  
Author(s):  
James Brean ◽  
Manuel Dall’Osto ◽  
Rafel Simó ◽  
Zongbo Shi ◽  
David C. S. Beddows ◽  
...  
Keyword(s):  
Sulfuric Acid ◽  
Antarctic Peninsula ◽  
Particle Formation ◽  
Open Ocean ◽  
New Particle Formation ◽  
The Antarctic

scholarly journals Annually Urban Fractional Vegetation Cover Dynamic Mapping in Hefei, China (1999–2018)

2021 ◽  
Vol 13 (11) ◽  
pp. 2126
Author(s):  
Yuliang Wang ◽  
Mingshi Li
Keyword(s):  
Standard Deviation ◽  
Vegetation Cover ◽  
Vegetation Index ◽  
Dynamic Range ◽  
Dynamic Change ◽  
Estimation Model ◽  
Sparse Vegetation ◽  
Fractional Vegetation Cover ◽  
Dynamic Mapping ◽  
Multi Temporal

Vegetation measures are crucial for assessing changes in the ecological environment. Fractional vegetation cover (FVC) provides information on the growth status, distribution characteristics, and structural changes of vegetation. An in-depth understanding of the dynamic changes in urban FVC contributes to the sustainable development of ecological civilization in the urbanization process. However, dynamic change detection of urban FVC using multi-temporal remote sensing images is a complex process and challenge. This paper proposed an improved FVC estimation model by fusing the optimized dynamic range vegetation index (ODRVI) model. The ODRVI model improved sensitivity to the water content, roughness degree, and soil type by minimizing the influence of bare soil in areas of sparse vegetation cover. The ODRVI model enhanced the stability of FVC estimation in the near-infrared (NIR) band in areas of dense and sparse vegetation cover through introducing the vegetation canopy vertical porosity (VCVP) model. The verification results confirmed that the proposed model had better performance than typical vegetation index (VI) models for multi-temporal Landsat images. The coefficient of determination (R2) between the ODRVI model and the FVC was 0.9572, which was 7.4% higher than the average R2 of other typical VI models. Moreover, the annual urban FVC dynamics were mapped using the proposed improved FVC estimation model in Hefei, China (1999–2018). The total area of all grades FVC decreased by 33.08% during the past 20 years in Hefei, China. The areas of the extremely low, low, and medium grades FVC exhibited apparent inter-annual fluctuations. The maximum standard deviation of the area change of the medium grade FVC was 13.35%. For other grades of FVC, the order of standard deviation of the change ratio was extremely low FVC > low FVC > medium-high FVC > high FVC. The dynamic mapping of FVC revealed the influence intensity and direction of the urban sprawl on vegetation coverage, which contributes to the strategic development of sustainable urban management plans.

scholarly journals Mass balance of the Antarctic ice sheet 1992–2016: reconciling results from GRACE gravimetry with ICESat, ERS1/2 and Envisat altimetry

2021 ◽  
pp. 1-27
Author(s):  
H. Jay Zwally ◽  
John W. Robbins ◽  
Scott B. Luthcke ◽  
Bryant D. Loomis ◽  
Frédérique Rémy
Keyword(s):  
Mass Balance ◽  
Antarctic Peninsula ◽  
East Antarctica ◽  
Mantle Material ◽  
West Antarctica ◽  
Mantle Viscosity ◽  
Grounding Line ◽  
The Antarctic ◽  
Total Gain

Abstract GRACE and ICESat Antarctic mass-balance differences are resolved utilizing their dependencies on corrections for changes in mass and volume of the same underlying mantle material forced by ice-loading changes. Modeled gravimetry corrections are 5.22 times altimetry corrections over East Antarctica (EA) and 4.51 times over West Antarctica (WA), with inferred mantle densities 4.75 and 4.11 g cm−3. Derived sensitivities (Sg, Sa) to bedrock motion enable calculation of motion (δB0) needed to equalize GRACE and ICESat mass changes during 2003–08. For EA, δB0 is −2.2 mm a−1 subsidence with mass matching at 150 Gt a−1, inland WA is −3.5 mm a−1 at 66 Gt a−1, and coastal WA is only −0.35 mm a−1 at −95 Gt a−1. WA subsidence is attributed to low mantle viscosity with faster responses to post-LGM deglaciation and to ice growth during Holocene grounding-line readvance. EA subsidence is attributed to Holocene dynamic thickening. With Antarctic Peninsula loss of −26 Gt a−1, the Antarctic total gain is 95 ± 25 Gt a−1 during 2003–08, compared to 144 ± 61 Gt a−1 from ERS1/2 during 1992–2001. Beginning in 2009, large increases in coastal WA dynamic losses overcame long-term EA and inland WA gains bringing Antarctica close to balance at −12 ± 64 Gt a−1 by 2012–16.

scholarly journals An Assessment of ERA5 Reanalysis for Antarctic Near-Surface Air Temperature

Atmosphere ◽  
2021 ◽  
Vol 12 (2) ◽  
pp. 217
Author(s):  
Jiangping Zhu ◽  
Aihong Xie ◽  
Xiang Qin ◽  
Yetang Wang ◽  
Bing Xu ◽  
...  
Keyword(s):  
Linear Relationship ◽  
Antarctic Peninsula ◽  
Austral Summer ◽  
Correlation Coefficients ◽  
East Antarctica ◽  
West Antarctica ◽  
Austral Spring ◽  
Reanalysis Dataset ◽  
Near Surface ◽  
The Antarctic

The European Center for Medium-Range Weather Forecasts (ECMWF) released its latest reanalysis dataset named ERA5 in 2017. To assess the performance of ERA5 in Antarctica, we compare the near-surface temperature data from ERA5 and ERA-Interim with the measured data from 41 weather stations. ERA5 has a strong linear relationship with monthly observations, and the statistical significant correlation coefficients (p < 0.05) are higher than 0.95 at all stations selected. The performance of ERA5 shows regional differences, and the correlations are high in West Antarctica and low in East Antarctica. Compared with ERA5, ERA-Interim has a slightly higher linear relationship with observations in the Antarctic Peninsula. ERA5 agrees well with the temperature observations in austral spring, with significant correlation coefficients higher than 0.90 and bias lower than 0.70 °C. The temperature trend from ERA5 is consistent with that from observations, in which a cooling trend dominates East Antarctica and West Antarctica, while a warming trend exists in the Antarctic Peninsula except during austral summer. Generally, ERA5 can effectively represent the temperature changes in Antarctica and its three subregions. Although ERA5 has bias, ERA5 can play an important role as a powerful tool to explore the climate change in Antarctica with sparse in situ observations.

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