Displaying 1 - 10 of 30
Operations
Chen, Siyu; Jiang, Nanxuan; Huang, Jianping; Zang, Zhou; Guan, Xiaodan; Ma, Xiaojun; Luo, Yuan; Li, Jiming; Zhang, Xiaorui; Zhang, Yanting. 2019. Estimations of indirect and direct anthropogenic dust emission at the global scale. [Atmospheric Environment] . 200: 50-60 DOI: https://doi.org/10.1016/j.atmosenv.2018.11.063
Uses Remote Sensing: yes
SEDAC Data Collection(s):
gpw-v3
(Journal Article)
export
Geldmann, Jonas; Manica, Andrea; Burgess, Neil D.; Coad, Lauren; Balmford, Andrew. 2019. A global-level assessment of the effectiveness of protected areas at resisting anthropogenic pressures. [Proceedings of the National Academy of Sciences] . 116(46): 23209-23215 DOI: https://doi.org/10.1073/pnas.1908221116
Uses Remote Sensing: yes
SEDAC Data Collection(s):
(Journal Article)
export
Han, Xian Xian; Li, Gao Yang; Lu, Wen Fang; Jiang, Yu Wu. 2019. Comparing statistical and semi-distributed rainfall–runoff models for a large subtropical watershed: A case study of Jiulong River catchment, China. [Atmosphere] . 10(2): 62 DOI: https://doi.org/10.3390/atmos10020062
Uses Remote Sensing: yes
SEDAC Data Collection(s):
ulandsat
(Journal Article)
export
Jin, Kai; Wang, Fei; Chen, Deliang; Liu, Huanhuan; Ding, Wenbin; Shi, Shangyu. 2019. A new global gridded anthropogenic heat flux dataset with high spatial resolution and long-term time series. [Scientific Data] . 6(1): 139 DOI: https://doi.org/10.1038/s41597-019-0143-1
Uses Remote Sensing: yes
SEDAC Data Collection(s):
(Journal Article)
export
Liu, Xue; de Sherbinin, Alex; Zhan, Yanni. 2019. Mapping urban extent at large spatial scales using machine learning methods with VIIRS Nighttime Light and MODIS Daytime NDVI Data. [Remote Sensing] . 11(10): 1247 DOI: https://doi.org/10.3390/rs11101247
Uses Remote Sensing: yes
SEDAC Data Collection(s):
grump-v1
ulandsat
(Journal Article)
export
Lloyd, Christopher T.; Chamberlain, Heather; Kerr, David; Yetman, Greg; Pistolesi, Linda; Stevens, Forrest R.; Gaughan, Andrea E.; Nieves, Jeremiah J.; Hornby, Graeme; MacManus, Kytt; Sinha, Parmanand; Bondarenko, Maksym; Sorichetta, Alessandro; Tatem, Andrew J. 2019. Global spatio-temporally harmonised datasets for producing high-resolution gridded population distribution datasets. [Big Earth Data] . 3(2): 108-139 DOI: https://doi.org/10.1080/20964471.2019.1625151
Uses Remote Sensing: yes
SEDAC Data Collection(s):
gpw-v3
gpw-v4
grump-v1
(Journal Article)
export
Magory Cohen, Tali; Dor, Roi. 2019. The effect of local species composition on the distribution of an avian invader. [Scientific Reports] . 9(1): 15861 DOI: https://doi.org/10.1038/s41598-019-52256-9
Uses Remote Sensing: yes
SEDAC Data Collection(s):
gpw-v3
(Journal Article)
export
Meng, Jun; Li, Chi; Martin, Randall V.; van Donkelaar, Aaron; Hystad, Perry; Brauer, Michael. 2019. Estimated long-term (1981-2016) concentrations of ambient fine particulate matter across North America from chemical transport modeling, satellite remote sensing and ground-based measurements. [Environmental Science & Technology] . 53(9): 5071-5079 DOI: https://doi.org/10.1021/acs.est.8b06875
Uses Remote Sensing: yes
SEDAC Data Collection(s):
(Journal Article)
export
Morgan, Brett; Guénard, Benoit. 2019. New 30 m resolution Hong Kong climate, vegetation, and topography rasters indicate greater spatial variation than global grids within an urban mosaic. [Earth System Science Data] . 11(3): 1083-1098 DOI: https://doi.org/10.5194/essd-11-1083-2019
Uses Remote Sensing: yes
SEDAC Data Collection(s):
ulandsat
(Journal Article)
export
Ou, Jinpei; Liu, Xiaoping; Liu, Penghua; Liu, Xiaojuan. 2019. Evaluation of Luojia 1-01 nighttime light imagery for impervious surface detection: A comparison with NPP-VIIRS nighttime light data. [International Journal of Applied Earth Observation and Geoinformation] . 81: 1-12 DOI: https://doi.org/10.1016/j.jag.2019.04.017
Uses Remote Sensing: yes
SEDAC Data Collection(s):
ulandsat
(Journal Article)
export

Pages

Export results as CSVXML